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Scientific Review of Vaccine Safety Datalink Information 
June 7-8, 2000 

Simpsonwood Retreat Center 
Norcross, Georgia 


Orenstein: My name is Walter Orenstein. I’m Director of the National 

Immunization Program at CDC and I want to thank all of 
you for coming here and taking time out of your very busy 
schedules to spend the next day and a half with us. Not 
only do we thank you for taking time out, but for taking the 
time out on such short notice, and also putting up with what 
I gather those of us who are townies here didn’t realize, but 
apparently the biggest meeting in Atlanta which has taken 
up all the hotel space and all of the cars, so I think many of 
you have had to take taxis here. We appreciate you putting 
up with this, but at least we did arrange the weather nicely 
and you can look out occasionally and see some beautiful 
trees. 

I think I am particularly impressed with the quality of 
expertise. We truly have been able to get at very short 
notice some'of the most outstanding leaders in multiple 
fields. That will be important in interpreting the data. 

We who work with vaccines take vaccine safety very 
seriously. Vaccines are generally given to healthy children 
and I think the public has, deservedly so, very high 
expectations for vaccine safety as well as the effectiveness 
of vaccination programs. 

Those who don’t know, initial concerns were raised last 
summer that mercury, as methylmercury in vaccines, might 
exceed safe levels. As a result of these concerns, CDC 
undertook, in collaboration with investigators in the 
Vaccine Safety Datalink, an effort to evaluate whether 


Scientific Review of Vaccine Safety Datalink Information 


June, 2000 



there were any health risks from mercury in any of these 
vaccines. 

Analysis to date raise some concerns of a possible dose- 
response effect of increasing levels of methylmercury in 
vaccines and certain neurologic diagnoses. Therefore, the 
purpose of this meeting is to have a careful scientific 
review of the data. 

This is not a policy making meeting. Vaccine policy 
making will take place after this consultation as part of the 
Advisory Committee on Immunization Practices, or ACIP 
deliberations. For those who don’t know, vaccine policy 
for CDC is really set through the recommendations of the 
Advisory Committee on Immunization Practices, or the 
ACIP. Thus, this is a scientific review to evaluate the 
quality of the scientific data. Our goal is to assure our 
policies are based on the best available scientific 
information. 

This is what is called an individual simultaneous 
consultation. What that means is each consultant will be 
asked for their opinion publicly on questions which Roger 
Bernier will bring up in a few moments. 

Although it will be of interest to see if the individual 
consultants tend to agree on particular issues, there is not 
the need to reach complete consensus. Your individual 
opinions should be very useful to the ACIP as it deliberates 
afterwards on policy options with regard to mercury in 
vaccines. 

We hope you will participate in discussions, listen to the 
comments of others and form your own opinions during 
this day and a half meeting. 


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June, 2000 



Dr. Modlin: 


Dr. Stehr-Green: 


Dr. Stein: 


Dr. Saari: 


Dr. Word: 


Dr. Rennels: 


Again, we thank you very much for coming here and we 
look forward to a productive consultation. 

In order to start, since many of us don't know each other, 
perhaps if we could go around the room and introduce each 
other. Let me ask maybe John, if you want to start. 

Certainly. I am John Modlin. I’m Chair of the ACIP and a 
member of the faculty at Dartmouth Medical School. 

I’m Paul Stehr-Green. I’m an Epidemiologist by training. 

I am an Associate Professor of Epidemiology at the 
University of Washington School of Public Health and 
Community Medicine and I’m also a consulting 
Epidemiologist for the Northwest Portland Area Indian 
Health Board. 

I am Marty Stein. I am on the faculty of Pediatrics at the 
University of California, San Diego where I am a General 
Pediatrician as well as Behavioral Pediatric and I co- 
chaired the American Academy of Pediatrics recent 
practice guideline on the diagnosis and evaluation for 
ADHD. 

I’m Tom Saari, Professor of Pediatrics, University of 
Wisconsin in Madison and the Division of Infectious 
Diseases in Pediatrics. I’m also on the AAPCOID and I’ve 
represented liaison relationships to a number of national 
organizations. 

I’m Bonnie Word, I am at the State University ofNew 
York in Stony Brook. I am also a member of the ACIP. 

I’m Peggy Rennels, a pediatric infectious disease specialist 
at the Center of Vaccine Development, University of 
Maryland. I am a member of the ACIP and the AAP 
Committee on Infectious Diseases. 


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June, 2000 



Dr. Rapin: 


Dr. Sullivan: 


Dr. Clarkson: 


Dr. Roller: 


Dr. Smith: 


Dr. Johnson: 


Dr. Clover: 


Dr. DeStefano: 


Dr. Chen: 


I’m Isabelle Rapin. I’m a Neurologist for children at 
Albert Einstein College of Medicine. I’m interested in 
developmental disorders, in particular language disorders 
and autism most recently. 

I’m Kevin Sullivan. I'm an Epidemiologist at Emory 
University, with the Department of Epidemiology and the 
Department of Pediatrics. 

I m Tom Clarkson and I come from an area of frozen 
tundra in Rochester, New York. I’ve been associated with 
the mercury program through Rochester for a long time. 

Loren Roller, Pathologist, Immunotoxicologist, College of 
Veterinary Medicine, Oregon State University. 

I’m Natalie Smith, Director of the Immunization Program 
at the California State Health Department. 

David Johnson. I'm the State Public Health Officer in 
Michigan and a member of ACIP. 

I’m Richard Clover, present chair of the Department of 
Family and Community Medicine, University of Louisville. 
I’m a member of the ACIP. 

I’m Frank DeStefano, Medical Epidemiologist in the 
National Immunization Program. I’m the project director 
of the Vaccine Safety Datalink. 

I m Bob Chen, I'm Chief of Vaccine Safety and 
Development at the National Immunization Program at 
CDC. 


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Dr. Davis: 


Dr. Johnston: 


Dr. Bernier: 


Dr. Gerber: 


Dr. Mast: 


Dr. Howe: 


Dr. Phillips: 


Dr. Caserta: 


I’m Bob Davis. Pm one of the Associate Professors of 
Pediatrics and Epidemiology at the University of 
Washington. I am also one of the investigators. 

Pm Dick Johnston, Pm an Immunologist and Pediatrician, 
now at the University of Colorado School of Medicine and 
National Jewish Center for Immunology and Respiratory 
Medicine. Adverse events related to vaccines has been of 
particular focus and interest for me mostly through serving 
on a series of committees dealing with the relationship 
between the vaccine and punitive adverse events. 

Pm Roger Bernier, the Associate Director for Science in 
the National Immunization Program. 

Pm Michael Gerber, Pm a medical officer at the National 
Institute of Allergy and Infectious Diseases of the National 
Institutes of Health. Pm also a member of the American 
Academy of Pediatrics Committee on Infectious Diseases. 

Eric Mast, Pm a Medical Epidemiologist with the Hepatitis 
Branch at CDC. 

Barbara Howe, Pm in charge of the clinical research group 
for vaccine development for Smith Kline Beecham in the 
U.S. 

Bill Phillips from Seattle, Washington where Pm in private 
practice of Family Medicine. Pm here representing the 
American Academy of Family Physicians where I chair the 
Commission on Clinical Policies and Research. 

Vito Caserta, Pm the Chief Medical Officer for the 
Vaccine Injury Compensation Program. 


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Dr. Kurz: 


Dr. Pless: 


Dr. Clements: 


Mr. Schwartz: 


Dr. Myers: 


Dr. Guess: 


Dr. Brent: 


Dr. Blum: 


Dr. White: 


Dr. Weil: 


Xavier Kurz, I’m Physician and Epidemiologist from 
Brussels, Belgium. I’m representing the European Agency 
for the Evaluation of Medicinal Products. 

I’m Robert Pless, I’m a Medical Epidemiologist with the 
Vaccine Safety and Development Branch at the 
Immunization Program. 

John Clements, the Expanded Program on Immunization, 
WHO, Geneva. 

Ben Schwartz. I’m in the Epidemiology and Surveillance 
Division at NIP. 

Martin Myers, I’m the Acting Director of the National 
Vaccine Program Office. 

I’m Harry Guess. I’m head of the Epidemiology 
Department at Merck Research Labs. 

I’m Robert Brent from Thomas Jefferson University and 
the Dupont Hospital for Children. I’m a Developmental 
Biologist and a Pediatrician. 

I’m Mike Blum. I’m from Safety Surveillance and 
Epidemiology at Wyeth. 

Good morning, I’m Jo White from North American 
Vaccine. I’m in charge of clinical development and 
research there. 

I’m Bill Weil, an old Pediatrician who is representing the 
Committee on Environmental Health of the Academy at 
this moment. 


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Ms. Ray: 


Mr. Lewis: 


Dr. Jones: 


Dr. Egan: 


Dr. Deal: 


Dr. Pratt: 


Dr. Staub: 


Dr. Sinks: 


Dr. Hadler: 


Dr. Mawle: 


Dr. Rodewald: 


I’m Paula Ray, I’m with the Northern California Vaccine 
Study Center and I’m project manager for that site, for the 
VFC. 

I’m Ned Lewis. I’m the Data Manager at the Northern 
California Kaiser Vaccine Study Center. 

I’m Dennis Jones. I’m a Toxicologist and Veterinarian. 
I’m the Assistant Director for Science, Division of 
Toxicology, ATSDR. 

I’m Bill Egan, Acting Director for the Office of Vaccines 
Research and Review at FDA. 

My name is Carolyn Deal. I’m the Acting Deputy Director 
of the Division of Bacterial Products at CBER at the FDA. 

I’m Douglas Pratt. I’m a Medical Officer in the Office of 
Vaccines at FDA. 

I’m Ted Staub, I’m the Global Head of Biostatistics and 
Data Systems for Aventis Pasteur. 

My name is Tom Sinks. Tm the Associate Director for 
Science at the National Center for Environmental Health 
here at CDC and I’m also the Acting Division Director for 
the Division of Birth Defects, Developmental Disabilities 
and Disability Health. 

Steve Hadler, Medical Epidemiologist, National 
Immunization Program. 

I’m Alison Mawle, I’m the Vaccine Coordinator for the 
National Center for Infectious Diseases at CDC. 

Lance Rodewald, I’m a Pediatrician and Associate Director 
for Science in the Immunization Services Division at CDC. 


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June, 2000 



Dr. Cordero: 


Dr. Chu: 


Dr. Rhodes: 


Dr. Verstraeten: 


Dr. Oakes: 


Ms. Heaps: 


Dr. Orenstein: 


Dr. Bernier: 


Good morning, Jose Cordero, Deputy Director of the 
National Immunization Program. 

Susan Chu, Deputy Associate Director for Science, 
National Immunization Program. 

Philip Rhodes, a Statistician in the National Immunization 
Program. 

I’m Tom Verstraeten, EIS Office at National Immunization 
Program. 

I’m David Oakes, the Chair of Biostatistics at the 
University of Rochester. 

I’m Wendy Heaps, Health Communications Specialist with 
NIP. 

I’d like now to turn the meeting over to Roger Bernier who 
will give us a chronology of events, charge to consultants 
and talk about our Chairman and 

I believe the person has arrived with everyone’s folders. I 
apologize that we didn’t get them all here earlier this 
morning, but they should all be here now. You should each 
have a tent with your name on it and you should have a 
name badge. The information in there is just an agenda and 
a copy of the information that was handed out before the 
meeting. 

I also want to reiterate a couple of points made by Walt 
Orenstein. Number one, that we have assembled quite an 
impressive array of expertise for this meeting. Some of 
you wondered why you were invited and worried that you 
wouldn’t be able to provide good advice. We are not 
expecting any one person to be able to cover all of these 


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topics. As you can see, we have amassed quite an array of 
expertise, so we feel we have covered all the bases for the 
questions that will arise, but no one individual is expected 
to be able to comment on all of this. 

The other thing I want to say is to reiterate the thanks of the 
CDC and the National Immunization Program. For some 
of you who have made yourselves available, you were not 
available when you were telephoned and invited, but some 
of you have been willing to change your schedules to make 
yourselves available and we genuinely appreciate that. 


Dr. Bernier: Let me talk just a little bit about the procedures today. I 

hope you have all received an agenda, but very quickly to 
give you an idea and feeling for how this day and the 
meeting has been planned to unfold, that isn’t to say that is 
the way it is going to happen, but the critical presentation 
this morning is really the one by Tom Verstraeten, which is 
scheduled at eleven o’clock. We have some introductory 
presentations prior to that, but that is the critical 
presentation presenting the basic information. We have 
allowed an hour for discussion of that presentation. There 
is more discussion time at the end of the day if we need it, 
but we hope to get that presentation in with ample time for 
discussion before lunch. 

Then Bob Davis will give a presentation about results of 
chart reviews which is supplemental to Tom. Then an 
independent review by Phil Rhodes. Then a comment on 
biologic plausibility and consistency by Dr. Roller, then we 
will have the break and ample time for discussion. 

Tomorrow we begin with discussion for any residual 
questions, then we will get to the individual consultants 
opinions. You will be asked your opinions and we’ll go 
through that in a minute as to what the questions will be. 


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The hope will be that you can look at those questions today 
and maybe prepare some notes so that you can talk from 
your notes tomorrow. Then weTl give you a clean sheet as 
you may want to revise what you wrote after you hear 
peoples opinions. 

Then we will have a discussion about research and 
potential next steps at the end of the morning and then we 
will ask for your opinions again about what you think about 
any next steps. 

Walt, do you have those opinion sheets? Okay, they are in 
the folders. There should be two of them. As I said, one 
that you might want to fill out this evening and take notes 
on, and then a clean one for any revisions that you may 
want to make after that. 

There are about five or six different groups here. You may 
have figured that our from the introductions. I believe 
there are eleven consultants from CDC. There is also a list 
of participants. It has been distributed, so you should have 
a list of participants. There is a list of participants which 
identifies the eleven CDC consultants. They are the ones 
who will be asked to fill out these sheets. Others of you 
may want to do that. Feel free to do that, but you are not 
under an obligation to do it. 

Another thing I would like to mention is that we will have a 
rapporteur. Dr. Paul Stehr-Green who is an Epidemiologist 
who introduced himself earlier. Paul is going to serve as 
rapporteur and we have allowed a half hour for a summary. 
I don’t know if he will use it all, but he has a half hour to 
give a summary at the end of the second day tomorrow, so 
he may corral you now and then during the meeting to ask 
for clarification of some things or points that you have 
made. But you will know why because Paul has been 
asked to do that. 


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I briefly wanted to show people the immunization 
schedule. 


Dr. Orenstein: 


Dr. Bernier: 


Can I make a very quick announcement? In addition to this 
being a simultaneous individual consultation on the part of 
the CDC, this is also going to be the initial meeting of the 
ACIP work group on Thimerosal and immunization, and 
the work group at the moment will consist of the five 
voting members of the Committee that are here. We will 
almost certainly expand the work group prior to the full 
ACIP meeting in about two weeks, but it will be important 
for the work group to get together at this meeting. I am 
hoping that the five of us can get together after dinner this 
evening. We will find a place and begin to discuss the 
various options and lay out the options for the full 
Committee in two weeks. 

For some of you who don’t work with vaccines every day, 
some of the consultants, just to let you know the focus of 
this. We are not likely to focus on all the vaccines today, 
but the three that are going to be of primary interest 
because they are given early in life include the Hepatitis B 
vaccine, which is recommended in three doses, and the 
DTP vaccine, diphtheria, tetanus, pertussis, which you will 
hear about and also haemophilus influenza type B which 
you see here according to this schedule. There will not be 
much discussion today about polio, measles, mumps, 
rubella, varicella or Hepatitis A. These vaccines have not, 
and do not contain Thimerosal. The focus is going to be 
about Hepatitis B, DTP and H. flu vaccine. 

Now the other thing that I thought would be helpful is to 
try to provide a brief summary of the chronology of events 
that surround Thimerosal. This is not the first time some of 
us have heard of this preservative. 


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Basically there was a Congressional Action in 1997 
requiring the FDA to review Mercury in drugs and 
biologies. In December 1998 the Food and Drug 
Administration had called for information from the 
manufacturers about mercury in their products. 

There is a European group of regulation authorities and 
manufacturers that met in April of 1999 on this, who at that 
time noted the situation, but did not recommend any 
change. 

In the U.S. there was a growing recognition that the 
cumulative exposure may exceed some of the guidelines. 
There are three sets of guidelines that are much in 
discussion. One from ATSDR, one from FDA and one 
from the Environmental Protection Agency. These 
guidelines are not all exactly the same. There was a 
recognition that the cumulative exposure that children 
receive from vaccination may actually exceed at least one 
of the guidelines that is recommended, that of the EPA. 
That caused a concern which resulted in a joint statement 
of the Public Health Service and the American Academy of 
Pediatrics in July of last year, which basically stated that as 
a long term goal, it was desirable to remove mercury from 
vaccines because it was a potentially preventable source of 
exposure. And if it was able to be removed, that it should 
be removed as soon as possible. That goal was agreed 
upon. In the meantime, there was postponement 
recommended for the Hepatitis B vaccine at birth. Also at 
that time, the FDA had sent a letter to manufacturers asking 
them to look at the situation with their products to see what 
could be accomplished as soon as possible. 

There was a public workshop on Thimerosal in August of 
1999. Dr. Myers will tell you a little bit about that this 
morning. In September of 1999, one of the Hepatitis B 
vaccines had removed Thimerosal from the product, so the 


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Dr. Clarkson: 


Dr. Bernier: 


Dr. Johnston: 


recommendation was made to resume use of Hepatitis B 
vaccine at birth. 

Since that time, I believe in October of 1999 the ACIP 
looked this situation over again and did not express a 
preference for any of the vaccines that were Thimerosal 
free. They said the vaccines could be continued to be used, 
but reiterated the importance of the long term goal to try to 
remove Thimerosal as soon as possible. 

Since then, I don’t think there have been any major events. 
What has happened in the meantime is we have continued 
to look at this situation and that is what you are going to 
hear more about at this meeting. 

Are there any questions about any of this? 

Could we get copies of these transparencies that you are 
showing? 

Yes, we will arrange for that. 

The next thing I would like to do, I have asked Dr. Dick 
Johnston to chair this meeting and he has been very 
gracious to accept that invitation from CDC. So at this 
point I would like to turn the meeting over to Dr. Johnston 
who will chair the meeting and keep us on track as much as 
possible. Thank you. 

Thank you, Roger. Jameka urged you to relax and it is my 
responsibility to be sure you don’t relax during the 
presentations at least so that at the end, we who are 
consultants can vote with the greatest amount of 
understanding and knowledge of what the issues are. 
Otherwise, 1 am not going to take any more time. Marty, 
next? 


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Dr. Myers: 


Dr. Snyder: 


Dr. Walker: 


Dr. Johnston: 


In the meantime, I think Dixie Snyder has walked in. If 
anyone else has come in late that hasn’t been introduced, 
you might want to introduce yourselves. Dixie, would you 
like to say hello? 

Good morning. I’m Dixie Snyder, the Associate Director 
for Science at CDC and the Executive Secretary for the 
ACIP. 

I m Alex Walker, I’m Chair of the Epidemiology 
Department at the Harvard School of Public Health. 

A lot of you were at the conference that I’m going to 
summarize, so if I omit something or over interpret 
something, please jump in. 

The conference that Roger was alluding to was a hot, sultry 
couple of days at Bethesda at the Lister Auditorium last 
August where the National Vaccine Advisory Committee 
and the Inter-Agency Working Group on Vaccines 
convened a special meeting to consider Thimerosal in 
vaccines. Obviously a pertinent topic for this morning. 

I think one of the major take home lessons was that we 
should have had that meeting in advance of many of the 
public health decisions that were made last summer, 
although that wasn’t possible, but it would have been 
desirable to have a meeting such as we are having today to 
consider the data first. 


Thimerosal is in many vaccines because it is a preservative 
and it lowers the rate of bacterial and fungal contamination 
that may occur during the manufacturing process, 
packaging and the use of vaccines in the field. It is 
particularly a concern in multi-dose vials because of the 
issue of re-entry multiple times in the vials, and it is also 
important in the manufacturing process for a number of 


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vaccines including inactivated influenza and some of the 
earlier DPT vaccines, and is a constituent of all DPT 
vaccines, but not all DTAP vaccines. 

There are three licensed preservatives in the United States, 
Thimerosal, ethynyl and phenol. We won’t talk about the 
other two today, but I thought I should mention them. 
Thimerosal is the most active and it has been utilized in 
vaccines since the 1930’s. 

At the time of the meeting last summer, there was only one 
licensed product containing hepatitis B vaccine that did not 
contain Thimerosal. That was a combination vaccine with 
HIV that was intended for use in two months or older, so 
the issue was that all of the vaccines available for the birth 
dose contained Thimerosal. In addition, many of the 
DTAP vaccines and the HIV vaccines, many, but not all, 
contained Thimerosal. 

Thimerosal functions as an anti-microbial after it is cleaved 
into ethylmercury and thiosalicylate, which is inactive. It 
is the ethylmercury which is bactericidal at acidic PH and 
fungistatic at neutral and alkaline PH. It has no activity 
against spore forming organisms. 

There is a very limited pharmacokinetic data concerning 
ethylmercury. There is very limited data on its blood 
levels. There is no data on its excretion. It is recognized to 
both cross placenta and the blood-brain barrier. 

The data on its toxicity, ethylmercury, is sparse. It is 
primarily recognized as a cause of hypersensitivity. 
Acutely it can cause neurologic and renal toxicity, 
including death, from overdose. 

Because of the limited data for ethylmercury and its 
physical chemical similarities to methylmercury, it was the 


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consensus of the meeting that in the absence of other data, 
that chronic exposure to methylmercury would need to be 
used to assess any potential neurodevelopmental risks of 
ethylmercury, although it was recognized that we needed 
data specifically on ethylmercury. 

We learned a great deal about the toxicity of ethylmercury 
from animal studies, accidental environmental exposures, 
and studies of island populations who consume large 
amounts of predator fish that contain high concentrations of 
ethylmercury. 

We learned that ethylmercury is ubiquitous and that 
assessments of exposure by infants would need to include 
environmental exposures, maternal foods, whether the baby 
was nursed or not, as well as their exposure to vaccines. 

Specialists in environmental health have extrapolated from 
those types of studies to establish safe exposure levels, and 
this is an important emphasis I would like to make on 
chronic, daily exposure to ethylmercury that incorporate 
wide margins. That is three to ten fold to account for data 
uncertainties. 

As an aside, we found a cultural difference between 
vaccinologists and environmental health people in that 
many of us in the vaccine arena have never thought about 
uncertainty factors before. We tend to be relatively 
concrete in our thinking. Probably one of the big cultural 
events in that meeting, at least for me, was when Dr. 
Clarkson repetitively pointed out to us that we just didn’t 
get it about uncertainty, and he was actually quite right. It 
took us a couple of days to understand the factor of 
uncertainty in assessing environmental exposure, 
particularly to metals. 


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If methylmercury were applied as a surrogate for 
ethylmercury, then some combinations of vaccines, 
according to the recommendation that Roger showed us, 
could result in some children having organomercurial 
exposure that exceeded some of those guidelines. 
Specifically the EPA guideline. 

There were a number of things that we got a consensus on 
in that meeting. First is that there was no evidence of a 
problem, only a theoretical concern that young infants’ 
developing brains were being exposed to an 
organomercurial. 

We agreed that while there was no evidence of a problem, 
the increasing number of vaccine injections given to infants 
was increasing the theoretical mercury exposure risk. 

We agreed that the greatest risk for mercury exposure from 
vaccines would be to low birth weight infants and to infants 
bom prematurely. 

We agreed that it would be desirable to remove mercury 
from U.S. licensed vaccines, but we did not agree that this 
was a universal recommendation that we would make 
because of the issue concerning preservatives for delivering 
vaccines to other countries, particularly developing 
countries, in the absence of hard data that implied that there 
was in fact a problem. 

There were a lot of uncertainties that we left the meeting 
listing. The first was chronic versus episodic exposure, 
oral versus parental exposure, ethyl versus methylmercury, 
the dose of mercury on a per kilogram base at birth and 
subsequently the issue of pre-term versus term birth. 

We did then discuss both theoretical and real disease 
burden risk. We saw some compelling data that delaying 


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the birth dose of hepatitis B vaccine would lead to 
significant disease burden as a consequence of missed 
opportunity to immunize. 

We have since seen those initial recommendations in July a 
year ago, a reduction in appropriate use of hepatitis B 
immunoprophylaxis to infants bom to mothers who were 
hepatitis B surface antigen positive. 

Dr. Clarkson made the compelling point that delaying the 
birth dose from day one or two until two or six months later 
would have a limited impact on the cumulative mercury 
exposure, and the point was made that the potential impact 
on countries that have 10% to 15% newborn hepatitis B 
exposure risk was very distressing to consider. 

We concluded the meeting with a research agenda, and as 
that is on the agenda for tomorrow, Alison Mawle and 
Mike Gerber were on that panel so they can contribute 
specifically. 

A couple of issues that were raised and probably are worth 
raising in the context of what we are going to discuss in 
this consultation, what contribution does vaccine mercury 
play in the isolated communities where mercury exposure 
was examined very carefully? What are the 
pharmacokinetics of excretion of ethylmercury? And then 
at the end of the meeting ironically, Walt Orenstein asked 
the most provocative question which induced a great deal 
of discussion. That was, should we try to seek 
neurodevelopmental outcomes for children exposed to 
varying doses of mercury by utilizing the Vaccine Safety 
Datalink data from one or more sites? 

The discussion that followed that, and I did review the 
transcripts of this in preparation, is very interesting. Drs. 
Gerber and Clarkson especially, but a number of others of 


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us also, expressed grave concerns that the many 
confounding co-variables would make such data very 
difficult to evaluate. Dr. Halsey made a very impassioned 
plea that we do carefully controlled studies to in fact 
address the issues specifically, and that such studies be 
conducted by neurodevelopmentalists and environmental 
scientists employing specific endpoints of their study. 

I suspect that today we will consider many of those 
confounding variables from the Vaccine Safety Datalink. 

Finally I would like to mention one more issue. As you 
know, the National Vaccine Program Office has sponsored 
two conferences on metals and vaccines. I have just 
recounted a summary of the mercury, the Thimerosal in 
vaccines. We just recently had another meeting that some 
of you were able to attend dealing with aluminum in 
vaccines. 1 would like to just say one or two words about 
that before I conclude. 

We learned at that meeting a number of important things 
about aluminum, and I think they also are important in our 
considerations today. First, aluminum salts, and there are a 
number of different salts that are utilized, reduce the 
amount of antigen and the number of injections required 
for primary immunization. 

Secondly, they don’t have much role in recall 
immunization, but it would represent a significant burden 
to try and develop different vaccines for primary and 
subsequent immunizations. 

Aluminum salts are important in the formulating process of 
vaccines, both in antigen stabilization and absorption of 
endotoxin. 


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Dr. Johnson: 


Dr. Myers: 


Dr. Johnson: 


Dr. Myers: 


Aluminum salts have a very wide margin of safety. 
Aluminum and mercury are often simultaneously 
administered to infants, both at the same site and at 
different sites. 

However, we also learned that there is absolutely no data, 
including animal data, about the potential for synergy, 
additivity or antagonism, all of which can occur in binary 
metal mixtures that relate and allow us to draw any 
conclusions from the simultaneous exposure to these two 
salts in vaccines. 

Thank you very much. 

Marty, the ethylmercury has been painted with the 
methylmercury brush, and maybe we will discuss this later, 
but are they metabolized equivalently, exactly equivalently, 
partially, differently? 

I’m not sure that I’m confident to answer that. Dr. 
Clarkson, if I recall, when asked that question specifically 
at the mercury conference said that we should assume that 
their excretion was similar, but that might well not be the 
case. That would the worse case scenario. 

Well, we have a discussion tomorrow on biologic 
plausibility and maybe that will deal with that. Dr. 
Clarkson was quoted as saying that delaying HepB for six 
months or so would not affect the mercury burden, but I 
would have thought that the difference was in the timing. 
That is you are protecting the first six months of the 
developing central nervous system. Is that not? 

I probably should allow him to speak for himself, but my 
interpretation was that the health guidelines were 
established based on a chronic, every single day exposure, 
and that a single day exposure, if I can quote him 


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accurately, wouldn’t change the blood levels one 
femtogram. 


Dr. Clarkson: 


Dr. Johnson: 


Dr. Clarkson: 


Dr. Rapin: 


I’m not sure where this statement came from, but I’m glad 
you raised it. Since the dose is the same for each vaccine 
administration, clearly there is a body weight difference 
after six months, so the actual dose per tissue will be lower 
at six months. I’m not quite sure what the question was. 

Well, maybe Isabelle should do this. I don’t want to spend 
too much time, but the time of exposure, that is the central 
nervous system of a newborn and so forth, does that make a 
difference in the biologic plausibility related to central 
nervous system effects that are under consideration? 

It could make a difference certainly. The guidelines that 
Dr. Myers is talking about is based on prenatal exposures 
and perinatal exposures. As far as I know the literature, 
there just isn’t that much evidence one way or the other as 
to whether exposure shortly after birth or exposure at six 
months would make a difference. In theory it could, but I 
don’t know of any studies that have actually tested that. 

There is an issue that the pharmacokinetics might be 
different, too. Again, this is all animal work, but the 
animal studies suggested, for example, a suckling animal 
does not eliminate methylmercury until the end of the 
suckling period, and there is a mechanism on the study for 
that. So this is not known for humans. So there could be an 
age difference in the excretion rates. 

I am not an expert on mercury in infancy. The diseases 
that neurologists know about mercury in infancy have more 
to do with the peripheral nervous system than with the 
central nervous system. I know of at least one child that 
was exposed to mercury and developed a very severe 


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neuropathy, but I don’t know whether the child, if one 
would test her carefully, had any cognitive deficits. 

I don’t know if anyone has looked at the literature of the 
old Pinks disease which was present in the twenties or 
thirties when mothers wore shields that contained mercury. 
1 really don’t know, so I’m sorry. 

Dr. Snyder: I think the issue at the meeting that I thought Dr. Clarkson 

was telling us was that we were focused on the amount of 
mercury in a particular dose of vaccine, and we needed to 
think beyond that in terms of what that meant for blood 
levels and therefore tissue levels, and then specifically the 
target organs. If we look at that single does, let’s say of 
hepatitis B vaccine, that single dose was not going to 
ratchet up the blood level. Whatever it was, for 
background reasons from food intake of the infant or the 
mother, that one dose was not going to make a major 
change in blood levels, and therefore major changes in 
tissue levels. That’s the way I interpreted the statement at 
the meeting. Which is not to say it’s unimportant, but it 
was a small amount relative to all the other intake. 

Dr. Clarkson: As you know, there is a paper just published on this now 

which I guess many of you in pediatrics have a copy of 
now. That’s right, if you are given mercury day by day as 
the guidelines are based on, whether it’s EPA, ATSDR or 
FDA, these are based on a constant daily exposure and at 
least for adults it would take almost a year to get to study 
state levels. Whereas we are just considering one single 
dose for vaccines. 

But nevertheless, a single dose from vaccines can raise 
blood levels by a certain amount. We now have one paper 
showing that in fact it does and the level it is raised to is 
reasonable. It’s reasonable for what we would expect the 
dose to be and what the body weight should be, and these 


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Dr. Brent: 


Dr. Johnson: 


Dr. Brent: 


Dr. Johnson: 


of course are in very low birth weight infants that the report 
was on. 

It’s just the sensitivity of the central nervous system, based 
on the mechanism that’s involved in producing the end 
result. You know the thalidomide data taught us that 
autism is related to the high brain and it produces it in the 
22 nd day of gestation, while the central nervous system 
from the standpoint of mental retardation, its most sensitive 
period is in the eighth week to the fifteenth week. That’s 
when we see the neuro-maturation. 

You are talking about miolimitation. I don’t know of any 
data of whether there is a sensitive period miol ionization or 
if you have a high enough dose you can affect 
miolionization throughout the period of miol ionization. 

I think you have to recognize that each of the 
developmental problems that have been evaluated here 
have a different stage where they are most sensitive from 
environmental factors. 

Are any of them different from birth, term birth to six 
months? 

In Hiroshima, Nagasaki, you had severe mental retardation 
after 75 rads. If you give 75 rads to an infant, nothing will 
happen with regards to their central nervous system 
development. So you have this changing sensitivity 
throughout embryogenesis and early childhood 
development that makes it very difficult to generalize. 

So the answer is that we don’t know. Between birth and 
six months there is no reason particularly, based on data at 
least, to be concerned that shifting the exposures back 
toward birth is any more risky than waiting till six months. 


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Dr. Myers: 


Dr. Sinks: 


Dr. Myers: 


Dr. Weil: 


The one thing that was a take away from that meeting was 
that if there were an increased risk, it would be in the low 
birth rate and preterm infants. 

I wanted to ask an unrelated question, and this has to do 
with potentially looking at confounding as we go through 
this. You mentioned the issue of aluminum salts. I know 
it’s an issue, but I don’t know the specifics of it. I wonder 
is there a particular health outcome that has been of 
concern that is related to the aluminum salts that may have 
anything to do with what we are looking at here today? 

No, I don’t believe there are any particular health concern 
that was raised. It was raised as an issue, and clearly it’s a 
confounding issue in that exposure to vaccine includes 
exposure to things other than Thimerosal. 

Two things. One, up until this last discussion we have been 
talking about chronic exposure. I think it’s clear to me 
anyway that we are talking about a problem that is 
probably more related to bolus acute exposures, and we 
also need to know that the migration problem and some of 
the other developmental problems in the central nervous 
system go on for quite a period after birth. But from all the 
other studies of other toxic substances, the earlier you work 
with the central nervous system, the more likely you are to 
run into a sensitive period for one of these effects, so that 
moving from one month or one day of birth to six months 
of birth changes enormously the potential for toxicity. 
There are just a host of neurodevelopmental data that 
would suggest that we’ve got a serious problem. The 
earlier we go, the more serious the problem. 

The second point I could make is that in relationship to 
aluminum, being a nephrologist for a long time, the 
potential for aluminum and central nervous system toxicity 


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was well established by dialysis data. To think there isn’t 
some possible problem here is unreal. 

Dr. Johnson: Thank you, Bill, for your comments. As an old 

pediatrician, I had that same kind of feeling. That there 
must be a difference with age. 

Just to not leave that as a hanger though, our metal experts, 
and we had quite a collection of people. We held the 
aluminum meeting in conjunction with the metal lions in 
biology and medicine meeting, we were quick to point out 
that in the absence of data we didn’t know about additive 
or inhibitory activities. We should not conclude 
necessarily that they would be additive. I think that was 
Tom’s point, in the absence of a health endpoint, we 
needed to be very careful. But I did want to raise the issue 
because it was a major issue of discussion there, that we 
did have binary salt exposure and we probably needed to 
understand more about that. 

Dr. Johnson: Thank you very much, Marty. I’m sure we’ll hear more 

from Dr. Koller this afternoon. 

Frank DeStefano is going to introduce us to the Vaccine 
Safety Datalink Study. 

Dr. DeStefano: The analyses you will be discussing for most of the 

morning come from the Vaccine Safety Datalink. I’m 
going to give you a quick overview of what the project is 
and then some of the data. 

This is a project collaboration between the CDC’s National 
Immunization Program and four large health maintenance 
organizations listed here. Group Health Cooperative in 
Seattle, Northwest Kaiser in Portland and Northern and 
Southern California Kaiser. They have a current enrolled 
population between them of over 6 million people. 


Dr. Myers: 




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For a little history on the project, it was begun to have a 
large population to address primarily rare potential vaccine 
safety problems. It began in March of 1991 at three sites, 
then the Southern California site began contributing data in 
October of 1992. 

The size population of between zero and six years old, this 
will be cumulative, over the nearly ten years of the project, 
I think we’re probably over 2 million children now. 

The concerns about HMOs sometimes have to do with their 
representativeness, at least in terms of data that the HMOs 
have been able to compare with the areas that they serve, 
they tend to be fairly similar in terms of ratio, ethnic, 
characteristics, age and such. Then we have expanded to 
include adolescents and adults, but we won’t be discussing 
those today. 

So the Datalink, this is sort of a schematic of what we are 
talking about here. The study begins with computerized 
data that the HMOs collect primarily for administrative and 
medical care purposes. They are collected for different 
reasons. The goal of the Datalink is to try to use those data 
and combine them to do vaccine safety epidemiologic 
studies. 

There are three main types of data that we use. Automated 
vaccination records. These are computerized immunization 
tracking systems if you will. Some of these could be 
considered the prototype vaccination registries. Obviously 
that’s a key for doing vaccine safety studies. The other 
main source of data deals with identifying health outcomes. 
I will talk more about those in a subsequent overhead. 
Then another key component is getting information on 
patient care characteristics, such as date of birth, gender 
and particularly important are dates when members enroll 


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and disenroll and the population which is critical for 
keeping track of the population under observation. Those 
data are sent to us at CDC. Each of the members of the 
HMO have a unique identifying number that is used to link 
among the various data sources. 

We at CDC serve sort of as a data coordinating center. We 
combine the data from all the four HMOs and do some of 
the combined data preparations and some of the analyses 
which we will be talking about. 

As I mentioned, these are computerized data bases. I am 
sure you are all familiar with the potential concerns and 
limitations of computerized data bases. We have done 
quite a bit of validation, particularly of the vaccine data, 
and this is some results from three of the HMOs about the 
vaccines that are going to be of primary interest in today’s 
study. 

In Northern California Kaiser NCK, you can see what their 
sensitivity and positive predictive value is. What 
sensitivity means here is that if a vaccination was in a hard 
copy medical record or log, it was actually captured by the 
automated data system. This was done by doing some 
actual chart extractions and comparing what is in the 
computerized data base with the hard copy records. So for 
DTP, 98% of the time, if it was in a hard copy record it was 
captured in the computer data base in Northern California. 

The positive predictive value means if it is identified in the 
computerized data base, when you go to the hard copy 
record it’s there. Basically you can see these results. For 
Northern California there is very high agreement for all the 
vaccines of interest. 

For Group Health, which is going to be the other main 
HMO that contributes to these analyses, you see the 


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agreement is fairly high, although not quite as high. 
Hepatitis B tends to be a bit low, and I think that primarily 
is because of capturing the birth dose. The hospital’s HMO 
birth dose some times didn’t tend to get into the data bases 
as well in the early years. I think Tom in his presentation 
will probably have some more to say about this. 

This is primarily where we determine outcomes. The 
primary sources are hospital discharge diagnoses, and all 
HMOs have these. Then there are treatment records from 
clinics. For the conditions that we are talking about today, 
most of these are treated primarily in outpatient clinics and 
not all the HMOs had outpatient records. They were 
electronic records that they provided to us. It was most 
complete for Northern California and Group Health, so that 
is why you will see the analyses are restricted 
predominantly to those two HMOs. 

We also have emergency room visits and can get Death 
Certificate autopsy reports, and if need be we have a 
variety of ancillary data sources, but we did not use any of 
those for these current analyses. 

The sort of prototypical analytical approach is to use these 
computerized data. Here is a screening. Usually because 
of the problems with automated data in terms of the 
validity of the diagnoses, et cetera, the computerized data 
we use usually as a screening analysis. Primarily to see if 
there is any preliminary assessment of vaccine outcome 
associations, or sometimes it is used as a way to identify 
possible cases of a condition. Usually we go to a next step. 
We have found it is necessary to go to a next step for more 
detailed analysis, and usually this involves chart reviews. 
Some times it actually involves interviews of parents or 
patients. These more detailed chart reviews are necessary 
to validate the outcomes of interest to make sure what those 
computerized codes actually represent in terms of what was 


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written in the medical charts, or in terms if you come up 
with a more standardized case definition. Also to confirm 
when the date of occurrence was, or some times to get a 
more reliable onset or incidence date and to verify 
vaccination history. We tend to be more comfortable using 
the automated vaccination histories. 

Then importantly to get additional risk factors, 
confounding information or other information on clinical 
details. Basically what we have in the computerized data 
on risk factors is gender and date of birth in essence. 

I will just give a little background on how this analysis on 
Thimerosal developed. I guess after the August meeting in 
Bethesda that Marty has told you about, a Thimerosal 
working group was convened. Michael Gerber coordinated 
that working group and it included representatives from 
several public health service agencies, as well as people in 
academia and other organizations. Sort of an informal 
working group. As I understood it, the primary purpose 
seemed to be to come up with ideas for research to see if 
there was anything really to these theoretical concerns that 
had been raised about Thimerosal exposure. There were 
proposals about studies to look at what happens to body 
burden after vaccination. Michael may have some 
information on that later in the meeting. 

One of the proposals that was made was to do a study that 
we will be talking about today. Looking at using the 
Vaccine Safety Datalink Project to look and see if there 
was any association between Thimerosal exposures as 
estimated through vaccinations received and selected 
outcomes. We weren’t made aware of the concerns that 
had been raised at the August meeting. 

At first it took a while for some people to understand the 
concept in the Datalink. I think by the second conference 


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Dr. Johnson: 

Dr. Gerber: 

Dr DeStefano: 
Dr. Gerber: 


call this concept got quite a bit of support, and we were 
encouraged to develop a protocol and such, which we did. 
We developed a protocol in conjunction with some input 
from this working group, as well as the Vaccine Safety 
Datalink investigators. 

Basically the protocol called for a two phase study. The 
first stage was the screening of automated data for possible 
associations, and I want to emphasis this is what we will be 
talking about today. This was like a screening analysis. 
We did narrow down the conditions we were looking for to 
conditions that had been suggested to be related primarily 
to methylmercury, and those were primary neurologic, 
neurodevelopmental and renal outcome. Still within those 
there was a broad category of possible specific conditions 
and we didn’t know if any of them would really have an 
association. So the idea was to do this screening analysis 
of automated data to see if there was any hint of association 
with any specific conditions, then the thought would be if 
anything came out we would go to the next step and do a 
confirmatory study or hypothesis testing study. 

At the time we were thinking this would have been the 
usual chart review case control study. Since then in 
looking at the conditions that have seem to have come out 
as possible associations, I think we might rethink that 
strategy and hopefully we will have a chance to discuss 
what that phase two might be tomorrow morning. 

Thank you, Frank. Why don’t we go right into Tom’s 
presentation. 

Why did you choose these two of the four HMOs? 

They were the ones that had outpatient data. 

So the other two didn’t have outpatient data? 


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Dr. DeStefano: 


Right. 


Dr. Verstraeten: Good morning. It is sort of interesting that when I first 

came to the CDC as a NIS officer a year ago only, I didn’t 
really know what I wanted to do, but one of the things I 
knew I didn’t want to do was studies that had to do with 
toxicology or environmental health. Because I thought it 
was too much confounding and it’s very hard to prove 
anything in those studies. Now it turns out that other 
people although thought that this study was not the right 
thing to do, so what I will present to you is the study that 
nobody thought we should do. 

If I can have the next slide. Frank already mentioned to 
two phases that we originally considered for this study. 

The design of the first phase, the screening phase, were we 
were looking for signals was as follows. We set it up as a 
cohort study using this automated VSD data. The exposure 
was to be mercury from Thimerosal containing childhood 
vaccines assessed at different ages of the children. The 
outcome was a range of plausible, neurologic and renal 
disorders. As plausible as I could find from the literature, 
anything that I could not exclude among the neurologic or 
renal disorders to be connected to mercury. 

On the study of population, we selected children bom 
between 1992 and 1997. We started in 1992 because we 
saw that is when the data became complete for the different 
HMOs. 

They had to be bom into two HMOs. We have already 
talked about that. The next condition was for these 
children to be continuously enrolled during the first year of 
life. We wanted to make sure that we captured all the 
vaccines given in the first year of life. 


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Finally, we excluded children that didn’t receive at least 
two polio vaccines before the end of the first year of life. 
The idea here was that there is still children that are 
enrolled in the HMO, but may not be using the 
immunization facilities at the HMO. We thought that the 
polio would be the vaccine with the least contraindications, 
and two polio vaccines is what is routinely recommended, 
so we would exclude those children that had less than two. 

The little asterisk indicates that this last condition was not 
in the original protocol. We added it as we started 
discussing our first findings. 

There were some other children that we excluded. First, 
premature children. From the very start we said we were 
going to look at these children separately and there are 
specific reasons to do that. We know that premature 
children are not vaccinated in the same way as term babies. 
At the same time they are at higher risk for the outcomes, 
so we wanted to look at them separately. 

Hepatitis B immunoglobulins. I think that is pretty 
obvious. Those would be vaccinated for hepatitis B and 
would have a higher likelihood of the outcomes. 

Finally, we excluded children with congenital or severe 
perinatal disorders. That was also a condition that we 
added. It was not in the original protocol. The idea was to 
get as pure a group of children as possible. Children that 
we knew didn’t have any problems before or at birth. I will 
come back to discuss this group later on. 

The exposure we assessed cumulatively. We kept on 
counting the cumulative amount of mercury at different 
ages of the children. We calculated using these individual 
automated vaccination records and we assessed it at one, 
two, three and six months of age. We figured that the 


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earliest month of life would be the most sensitive to 
mercury, so we wanted to see what was going on during 
those months. 

Then after we calculated that, we categorized these 
exposures by levels of 12.5. 12.5 is the minimum amount 
that any Thimerosal-containing vaccine has and namely 
hepatitis B has 12.5 micrograms of ethylmercury. 

There is an assumption there that for the Hep vaccines, we 
weren’t sure of this beforehand, but we confirmed later on 
that Hep vaccines in our cohort all contained Thimerosal. 

Now for the outcomes, we looked at the neurologic and 
rental outcomes and we classified them into major 
categories. One of those is neurologic developmental 
disorders. Sometimes we refer to this category as NDD. 

In this category we have all the outcomes that received any 
of these codes, which are on this slide. I will not go over 
all of them, but they include such things as autism, 
stammering and Tics. The largest group in here is under 
315. That includes such things as speech and language 
disorders and coordination disorders. There is a very small 
group of mental retardation. 

Another category were all the renal disorders which we put 
altogether into one large category. That goes from 
glomerulonephritis, nephrotic Syndrome and to renal 
failure. The major single code being used here is 
unfortunately the one that is called unspecified kidney and 
ureter disease. 

Besides these two categories, we looked at some other 
neurologic disorders. Some of them we categorized in a 
group we called degenerative neurologic disorders, and 
then there was a final category of other neurologic 


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disorders, which we thought we could not put into one of 
the categories. It includes such things as epilepsy. 

For our statistical analysis we used proportional hazard 
models. These models were stratified over the two HMOs, 
year and month of birth. Originally we had only thought of 
the year of birth, but very early on some people commented 
that was not specific enough. That we should add the 
month of birth. So we should compare children that were 
bom in the same HMO, the same month and the same year 
so our cases and controls would come from within such a 
strain. Then we adjusted our analysis for gender. That is 
the only covariant we adjusted. 

For each of these disorders Tve mentioned before, we did a 
separate analysis. If we found within the cohort at least 50 
cases, which was a very rough sample size estimate to 
detect, a relative risk of 2, so we said any disorder for 
which we find at least 50 cases we will do a separate 
analysis. All the other disorders we will just include in the 
overall category, but we will not look at them separately. 

Now turning to the results. These are the number of 
children that we found. First of all, bom in any of the two 
HMOs in that time period, we found a little more than 
200,000 children. 

This condition of being continuously enrolled eliminated 
quite a large number of those and we were left with 
140,000. There was only a few thousand that didn’t get 
their two polio vaccines by one year. There was about 5% 
premature children. There were very few children that 
received hepatitis b immunoglobulins and finally there was 
quite a large group, about 25%, that we excluded because 
of congenital or perinatal disorders. So we were finally left 
with about 1 10,000 children in our cohort. 


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Turning to the exposure, this is the different vaccines that 
contribute to the exposure at three months of age. I will 
focus a lot at this exposure of three months of age for 
reasons I will show you later on, mostly because it has the 
nicest distribution. At three months of age, children have 
from zero to over 75 micrograms of ethylmercury exposure 
from Thimerosal-containing vaccines. Zero, that’s pretty 
obvious. They didn’t get any vaccines. That’s another 
important point to keep in mind. None of the vaccines, 
except for polio which is usually given together with DTP 
or haemophilus influenza, was Thimerosal free in our 
cohort. That means if the children don’t have Thimerosal, 
it means they didn’t receive any vaccines. Whether it’s 
one, two, three or six months of age. 

The next category would be the children that received one 
hepatitis B. One up from there would be the children that 
received two hepatitis B vaccines and no DTP or no Hib, 
which is haemophilus influenza. Or there is another 
possibility. There is DTP and HIB exist in a combination. 
It’s called Tetramune. This vaccine contains 25 
micrograms of ethylmercury, so it’s only half of what the 
children get than when they get DTP and HIB separate, 
they get 50. If they get those two combined, they get 25. 

The next category would be the same combination plus one 
hepatitis B. 

**** Now at fifty, there is another two possibilities. Children 

can have received two hepatitis B vaccines before three 
months, and this combination vaccine, or no hepatitis B 
and the DTP and HIB separate, which I mentioned is 25 
each. So that would add up to 50 also. 

This combination vaccine was used only in one HMO, at 
Northern California Kaiser. In Group Health they don’t 
use it. In Northern California Kaiser, the large majority of 


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children received the combination vaccine. That’s why 
most of the children at Northern California Kaiser has a 
much larger contribution to this cohort than Group Health. 

Finally the two top categories are both had one DTP and 
one H1B separately, combined with one hepatitis B or two 
hepatitis B. There are very few children that get more than 
75 at three months. That would occur if they get more than 
one DTP or more than one Hib, together with two hepatitis 
B vaccines. 

I know this slide is a bit busy, but if we take our time I 
think it will make sense. It’s the distribution of 
ethylmercury from Thimerosal-containing vaccines at one, 
two, three and six months of age. This first part of the slide 
with the small numbers is the distribution at one month of 
age. Basically the distribution at one month is whether or 
not the child received hepatitis B or not. If they didn’t 
receive hepatitis B, there was no mercury. If they received 
it, it was 12.5. 

There is a few children who received their first DTP or 
their first HIB before they finished the first month of life, 
which I cut off as 3 1 days. So basically at the first month it 
is a dichotomous variable. 

Going to two months, the distribution is quite similar. 
There are a few children who already received their DTP 
and HIB and possibly a second hepatitis B, but still the 
largest majority is in these categories. 

At three months of age we get what resembles most normal 
distribution. Those are the categories which I’ve discussed 
with you on the previous slide, whereby the largest group is 
anywhere from 37.5, 50 or 62.5 micrograms of mercury. 
There is a few children in these low categories and there 
are very few children above 75. 


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At six months, the distribution becomes multi-modal with 
severe peaks at different levels of mercury. 

Now what happens with our exposure over time. We’ll 
probably talk a lot about temporal trends. This is the 
average mercury exposure at different months of age for 
the entire VSD cohort. Not just our cohort, but that 
includes two other HMOs. What happens over time is that 
between 1991 and 1992 there is a raise at all levels, which 
is due to hepatitis B. In 1991 hepatitis B was not much 
used in newborns. It was introduced mostly in 1992 and in 
some HMOs a bit later, and that’s why we have this 
increase. 

We have a small decrease after that which is mostly due to 
the introduction of this DTP-HIB combination vaccine, 
which reduces their cumulative mercury level. 

In the end we have a slight increase again which happens 
when DTaP, the acellular DTP vaccine has introduced. 
That one did not exist in combination, or that was used 
very little. That made some of the HMOs go back to 
giving DTP and HIB separately, and that increased the 
levels of mercury again. There are some other factors that 
play a role, but most those changes would be due to those 
policy changes. 

However, if we look at HMO by HMO, those trends are not 
as stable as they look for the entire VSD. This is for Group 
Health. This is only looking at three months of age. Those 
categories where I have lumped together, those categories 
below are equal to 25 micrograms. The one that jumps out 
mostly is the highest category, equal to or higher than 75 
micrograms. What we will also notice here is that at Group 
Health, the exposure is higher than the other HMO, 
Northern California Kaiser. So at Group Health there is a 
jump from ‘92 to ‘93 for the highest category, and after that 


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it doesn’t change much. It goes back down again in 1997. 
Basically the purpose of this slide is to show that those 
exposures are not entirely stable over the different years. 

This is the same for Northern California Kaiser. What we 
see is that the most prevalent categories here are the ones 
of 37 and 50 micrograms at three months of age, indicating 
that the level of exposure at Northern California Kaiser is 
not as high as Group Health. Also the trends do vary a bit 
over time. 

Turning to the outcomes, here are some crude data of the 
outcomes. The first slide shows you the total numbers for 
some of the outcomes by year of birth of the children. This 
is the total number of year of birth of the children, which is 
rather stable. 

These are the numbers for the entire category of neurologic 
developmental disorders, where we see that it is basically 
the children bom in the first years of our cohort, ’92 to ’95, 
who are contributing mostly to our outcomes, which is not 
surprising because the other children are just not old 
enough to be diagnosed with any of these disorders. 

For speech, that’s the same. 

This is attention deficit disorder, which is another outcome 
on which we shall focus quite a bit. It’s the same trend. 
It’s mostly the children bom in the earliest years. It’s even 
more so for ADD where the children have to be older to be 
diagnosed. So it’s good to bear in mind for some of the 
outcomes, we’re talking mostly children bom in the earliest 
years of our cohort. 

This slide gives you the crude rates of the outcomes by 
level of exposure at three months of age. So what we have 
here are the categories of exposure at three months of age. 


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The numbers of each category which I showed you which 
vaccines are in each category, and now how many cases did 
we find in each category, followed by the rates which is 
taking in account person time, so these are rates by 1,000 
person years. 

I’ll leave it up to you to think whether there are trends, yes 
or no. What is important to notice here is we have 
combined Group Health and NCK on this slide. On the 
next slide I will show you what happens when we separate 
them out. 

Again, this is the entire category of neurologic 
developmental disorders. This is speech delay. This is 
attention deficit disorder. None of these numbers are in the 
text that you have received before coming here. 

The purpose, many times we have been asked to provide 
the raw data to have a sense of what is going on and which 
numbers we are talking about. We are not looking in much 
detail at this time at these different rates. 

For ADD, these three categories are lumped because the 
numbers become quite sparse. 

This is what happens when we separate the two HMOs. 
What is important to notice is first of all, the overall 
incidence rates between the two HMOs differ substantially 
for some of the outcomes. We have a much higher rate of 
speed at Group Health compared to Northern California 
Kaiser. For attention deficit disorder, that number is not as 
high. 

Secondly, the rates year by year or any trends that you 
might think one way or the other way can be quite different 
between the two HMOs. That is true for both disorders we 
have selected. One of the reasons we keep selecting these 


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disorders is because they have the most cases, so we avoid 
getting sparse results and we think some of the findings are 
significant for these disorders. 

In summary, what we wanted to say about the data that 
we’ve shown you is the exposure varies quite a bit by 
HMO and over time. Secondly, the outcomes or the 
incidence of the outcomes also varies by HMO and time. 
Therefore, we think it is quite difficult to interpret crude 
results. If we come up with basic 2x2 tables, there would 
be a lot of confounding that we don’t take into account. 
Therefore, we think we have to account for these different 
trends and differences by HMO in whichever risk analysis 
we do. 

Now turning to the results of our proportional hazard 
models, we have compared in total 17 individual out of the 
38 plausible outcomes. Meaning that 17 of those had at 
least 50 cases. Three of the grouped ones also had that 
number. We’ve compared those outcomes to seven 
different measures of exposure. The seven measures of 
exposure are in the text. They are the continuous measure 
at one, two, three and six months of age. Those are four, 
then there is the categorized exposure at three months of 
age. Finally, we have also included the dichotomized 
exposure at one and three months using the EPA limits as a 
cut off to difference between height or low exposure. That 
gives us seven measures of exposure. 

From those risk analysis, excluding those dichotomized for 
EPA, we have found statistically significant relationships 
between the exposure and the outcome for these different 
exposures and outcomes. First, for two months of age, an 
unspecified developmental delay which has its own 
specific ICD9 code. 


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Exposure at three months of age. Tics. Exposure at six 
months of age, an attention deficit disorder. Exposure at 
one, three and six months of age, language and speech 
delays which are two separate ICD9 codes. Exposure at 
one, three and six months of age, the entire category of 
neurodevelopmental delays, which includes ail of these 
plus a number of other disorders. 

Now going into detail of some of these. The slides I will 
show now, they were also all in the original text which you 
have received. The results of the risk calculations for the 
exposure at three months of age are categorized into seven 
categories by 12.5 micrograms, and the last one is any 
exposure about 62.5 which is basically 75 micrograms. 

The reference category in this calculation is the zero 
microgram category. In other words, the children that 
didn’t receive vaccines. 

For each of these categories what is shown is a point 
estimate and a 95% confidence intervals. Then these point 
estimates are linked by a continuous line to visualize a 
potential trend. 

For each category I have shown here the number of cases 
for each category. Finally, this is a test for trend of these 
findings, which I have done by taking the exposure as a 
continuous variable. It gives you the 95% confidence 
intervals and the P value for the finding. 

For the overall category of neurologic developmental 
disorders, the point estimates of the categorized estimates 
suggest potential trends, and the test for trends is also 
statistically significant above one, with a P value below 
0.01. The way to interpret this point estimate which seems 
very low is as follows. That’s an increase of .7% for each 
additional microgram of ethylmercury. For an example, if 


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Dr. Oakes: 

Dr. Verstraeten: 
Dr. Sinks: 


Dr. Verstraeten: 


Dr. Rapin: 


Dr. Verstraeten: 


we would go from zero to 50 micrograms of ethylmercury, 
we would have to multiple these estimate by 50, so that 
would give us an additional increase of about 35%, which 
is pretty close to the point estimate for this category. Or 
for the overall, we would have to multiple 75 micrograms 
to .7 and that would give us about one and a half for the 
relative risk. 

If anyone has questions on this graph I will take them now 
because the next slides have similar slides and I think it is 
important to understand what these graphs represent. 

I take it you are only counting out after three months then? 

Absolutely. 

If I remember your exposure distribution, they were 
increasing not in actual micrograms, but in clumps because 
of the way the dose is applied. I wonder if it’s appropriate 
to follow this using micrograms versus those actual doses, 
because you’re trying to fit the model where it actually 
isn’t quite getting the finalized projection. 

I think you have a point. I think one other point would be 
to just do it by 12.5 micrograms. I have done that and it is 
almost identical. 

At what age were these behavioral diagnoses made because 
that’s a major issue? 

Most of them start from about two years of life and 
depending on the specific outcome, I think I have given 
you in the text you have received, the mean age for any of 
these outcomes. You will see that it varies. I think the 
speed, they are a bit younger. The attention deficit 
disorder, they are a bit older. But one thing is for sure, 
there is certainly under-ascertainment of all of these 


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Dr. Walker: 


Dr. Verstraeten: 


Dr. Rapin: 


Dr. Verstraeten: 


Dr. Davis: 


because some of the children are just not old enough to be 
diagnosed. So the crude incidence rates are probably much 
lower than what you would expect because the cohort is 
still very young. 

Following up on that, since you have a substantial part of 
the cohort which hasn’t lived through the periods during 
which these diagnoses might be made most commonly, an 
elevate in association here could also simply represent a 
bringing forward in time of a diagnosis associated with a 
particular vaccination pattern. So something which would 
have been censored now moves into your observation 
period. 

That’s absolutely true. I cannot differentiate between 
whether it’s an overall increase or whether it’s just bringing 
it forward. I agree. 

How did they make these diagnoses? You tell me that 
they’re coded in the database, but how were the diagnoses 
made? 

What I am presenting to you now is just the results of the 
automated data. That means I don’t know anything about 
how these diagnoses were made at this point. What we will 
present to you this afternoon is some of the results of the 
chart abstractions. I think at that stage we are in a better 
position, at least for some of the outcomes, to tell you how 
they were diagnosed. 

Just to follow up on that, even when we get to that point 
what we are left with is sort of the real worldwide 
distribution of diagnostic patterns. So nowhere today or 
tomorrow will you ever hear that an analysis restricted to 
children that were carefully examined in the 
neuropsychiatric clinic. These are kids who are seen by 
regular old pediatricians who might eventually get referred 


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Dr. Verstraeten: 


Dr. Chen: 

Dr. Verstraeten: 


Dr. Stehr-Green: 


to a speech pathologist or attention deficit specialists, but 
the original coding is a pediatrician or a family physician 
who is making the diagnosis. 

Just for the sake of the presentation, could I go on? 
Because I see that we are going into questions about other 
issues. 

This graph shows you a similar result for attention deficit 
disorder. One difference from the previous graph is that 
here the reference category is older children that received 
less than 37.5 micrograms of ethylmercury at three months 
of age. I did this because the numbers become so small 
that the estimates almost explode for some of these 
calculations. So for some of the disorders where the 
numbers are small, I have collapsed these three bottom 
categories and used that as a reference category. For 
attention deficit disorder we also have a suggestion of a 
trend. The test for trends is borderline, not statistically 
significant above one. 

Go back one slide. 

Tm sorry, we skipped one. This is the result for autism, in 
which we don’t see much of a trend except for a slight, but 
not significant, increase for the highest exposure. The 
overall test for trend is statistically not significant. 

Now for the speed delays, which is the largest single 
disorder in this category of neurologic developmental 
delays. The results are a suggestion of a trend with a small 
dip. The overall test for trend is highly statistically 
significant above one. 

I just want to point out that none of the point estimates for 
any dose level were statistically significant when you test 


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for trend. To what extent is that an anomaly based on the 
huge fact finding? 

Dr. Verstraeten: I think that is an important point that we will have to 

consider later on. 

Here we do have one, but that’s quite rare. What this 
represents is the overall category of developmental delays, 
of which I have excluded the speed delays because the 
impression we had was that some of the calculations were 
driven by this speech group, which was making up about 
half of this category. After excluding this speech group, 
this trend is also apparent in this group and the test for 
trend is also significant for this category excluding speech. 

This is an example where there is rather a suggestion of a 
negative trend, however the test for that trend is not 
significant. There is a decrease for the highest category for 
cerebral palsy. 

For the renal disorders, there is also not much of a trend, 
except for a slight decrease here for the highest category. 
The overall test for trend is non-significant, below one. 

This shows you the results for premature children for the 
entire category of neurologic developmental disorders. 
What we see here is there is a very significant drop from 
children that were not vaccinated to children that received 
the minimum amount of Thimero sal -containing vaccine. 
After that there isn’t much of a trend. The overall test for 
trend, which I think is in the text, is significantly negative. 
That is driven by this finding here. What happens here is 
that these premature children which are at high risk of 
having a disorder, or that is what we assume, are simply 
not being vaccinated and that results in an artificially high 
estimate for this zero group. However, what is also 
important to note is that after that we don’t have much of a 


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trend happening there. That is one of the consistencies that 
we will have to discuss later on. 

Now some results of when we tried to assess exposure by 
birth rates. We have birth rates for about 10% of the 
children in this cohort. That was done by linking the VSD 
to the states Birth Certificate files. It is only available for 
one HMO, for Group Health Cooperative, and it is only for 
about two-thirds of the children of that HMO. 

What we have are the crude numbers again. Now I have 
divided the cumulative mercury level at three months of 
age by birth rates. Then I have categorized that exposure 
into different categories. I have tried to approximate 
quantals as much as possible, while keeping comparable 
categories. So it goes from zero to 14 because there are 
very few children with zero. Then 15 to 17, 18 to 20, 21, 
23 and then above 23. The numbers in each category are 
comparable. These are the number of cases for the one 
category and two of the major outcomes. Then the rates. 
These are not adjusted for person time, it’s just crude rates. 
It’s just this number divided by this number which gives 
you this percentage. 

What I have done for these two categories in the category 
of outcomes is first of all, I have looked at what is the 
influence of birth rates on the outcome itself? What we see 
is that for attention deficit disorder, this is not significant. 
Below one means the lower the birth rates, the more likely 
to get the outcome which is what we would expect for most 
of these disorders. 

For speech, that does not happen. This is a strange finding. 
That the heavier babies in this cohort are more likely to 
have the outcome, and that is statistically significant. 


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For the overall category of developmental disorders, the 
estimate is below one, but it’s not significant. 

The next estimate I would like to point out is this one here. 
What happens if we divide the cumulative exposure by the 
birth rates? For attention deficit disorder, this estimate is 
near or a little bit higher than the one we had for the 
cumulative birth rate plain or not dividing by the birth rate. 
So it doesn’t affect it very much and the confidence 
intervals overlap one. 

What happens for speech, however, where this estimate for 
cumulative mercury exposure was significantly above one, 
it now goes below one. Although it’s not significant, the 
significance disappears and the direction of the relationship 
becomes negative. 

For the overall category of developmental disorders, we 
have a similar finding to the attention deficit disorder 
where this estimate slightly increases and the significance 
slightly increases also. However, we have to be careful in 
comparing this estimate to the one where we haven’t 
divided by birth rate because we have a different scale. So 
it’s not because it becomes somewhere around 7 or 8 to 25. 
That means an increase. More important would be the 
level of significance, which has only slightly increase. 

Now a different approach. Instead of dividing the 
cumulative exposure by birth rate, is looking at the 
cumulative exposure and stratify the analysis on birth rates 
to see if that makes any difference on our findings. I have 
stratified by categories of 250 grams of birth rates. What 
happens if I do that the estimate, which I think before 
stratification was about 007 or 008, is hardly affected. 
Also for speech, after stratifying on birth rates, the estimate 
is not very much affected. So we have two quite different 
findings. If we stratify on birth rate it hardly affects the 


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estimate. If we divide by birth rates, it does affect the 
effect and I think we could have some interesting statistical 
or biostatistical discussion about this phenomenon. 

For the overall category, stratification doesn’t really affect 
the estimate very much, and dividing gives you a similar 
result if you take into account the different scales. 

Now turning to the main limitations of this study. First of 
all, there is potential misclassification of exposure. Frank 
has mentioned that the hepatitis B birth dose can be missed 
for some children. I have looked at details of that and that 
is some of the additional analysis we could look at later on. 

Thimerosal in haemophilus influenza vaccine originally 
were not shown, but we’ve been working together with 
people from the FDA and they have used the lot numbers 
that we have for each individual vaccine that is given. We 
have the lot numbers and we have sent those lot numbers to 
the people at the FDA and so far they have told us that less 
than 1% of the vaccines in our cohort, of the Hep vaccines 
in our cohort are Thimerosal containing. Less than 1% are 
Thimerosal containing, so everything all the others are 
Thimerosal free. 

There is a difference in packages. If they are packaged in 
vials with 10 doses, they are Thimerosal containing. If 
they are packaged in vials with one single dose, then they 
+*0? are Thimerosal free. 

Dr. Chen: Most of the vaccine that was used in the study contained 

Thimerosal. 

Dr. Verstraeten: Right, so it was multiple dose vials. If it is single dose 

vials, it is Thimerosal free and hardly any of that was used 
in this cohort. 


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The birth rate information, we only have this on less than 
10% of the cohort, so that information is limited. 

There is the issue of using ICD9 codes for the outcome and 
someone already raised a concern about this. 

There is the issue of medical care utilization factors. One 
of the main worries or one of the biases that we are 
particularly worried about is that the same parents that 
bring their children for vaccination would be the same 
parents that bring their children for assessment of potential 
developmental disorders. That could drive the estimates 
that we are seeing. There are a number of ways we have 
been trying to look at this, and we can look at that in the 
following discussions. 

It’s not just the parents, but it’s also the health care 
providers. There is a potential that certain health care 
providers use more hepatitis B at birth and would also be 
more likely to diagnose some of the outcomes. 

There is the issue that in the VSD we can only look at dose 
outcomes that come to medical attention. There is no 
routine screening of children, so it is only if the mothers 
bring their children for a problem that we will be able to 
pick it up. 

Finally, for some conditions we didn’t have sufficient 
power. That is particularly true for the rental disorders. 
We have very few cases in that category, so our bar is quite 
low. 

There is inconsistency of our findings among premature 
infants. That is an important point. 

There is the issue of excluding congenital and perinatal 
disorders. That has raised some concerns. 


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There is the question of variation and exposure. What does 
it mean exactly if a child has a low exposure or a high 
exposure? Basically because all vaccines have Thimerosal, 
it is a difference in being on time with your vaccination 
schedule. At three months of age some kids have received 
more vaccines than others, so what we are looking at is 
how well the children are following their prescribed 
regimen of vaccinations. 

Finally, and this may be the toughest one of all, how do we 
know that it is a Thimerosal effect? Since all vaccines are 
Thimerosal containing, how do we know that it’s not 
something else in the vaccines such as aluminum or the 
antigens? 

In conclusion, the screening analysis suggests a possible 
association between certain neurologic developmental 
disorders. Namely Tics, attention deficit disorder, speech 
and language disorders and exposure to mercury from 
Thimerosal containing vaccines before the age of six 
months. No such association was found for renal disorders. 

Dr. Rennels: Do you have the data to show us of exposure at six months, 

or so fathered, by just saying three months because it is a 
nicer distribution? 

Dr. Bernier: Let me explain a little bit about how we structured this. 

We’ve presented this a couple of times and in the past 
people have raised questions. We have done analyses of 
these questions. We have presented the whole talk and the 
results of those analyses. We have found that it 
overwhelms everybody. So what we have decided to do is 
just do the regular abridged presentation, which he has just 
finished, and as you raise questions he will pull out of his 
question and answer bank. If you hit on a question that 
someone else has already raised, he will probably have the 
analysis. I trust that you will not think of all the questions 


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that others have raised, but on the other hand you will think 
of new ones that we haven’t done already, so that’s what 
they are getting ready to do. I think learning as you go may 
be a better way to digest this information. 

Dr. Verstraeten: What I have for six months is only looking at the exposure 

as a continuous variable. I have not reproduced this 
graphs. Unfortunately that is quite a bit of work, but if that 
is what you are asking I don’t have that. I just have the 
continuous variables and these results were in the text you 
received. So basically what we are seeing is that for the 
ones that we are particularly concerned about, the speech 
and the overall category, these are also significant for 
those. It is also significant for the language delay. 

For some, like attention deficit disorder, it only becomes 
significant at six months. For others like Tics, it looses the 
significance by six months. However, one thing you have 
to bear in mind, there is a high correlation between the 
exposure at three months and six months of age, which is 
what you would expect. Once the children get their 
vaccines early in the first three months of life, they are also 
more likely to get them earlier in the following three 
months. 

I have a slide with these correlation coefficients, but it’s 
probably not worthwhile in looking at those figures 
specifically. But what we see is that the correlation is very 
high between three and six months, but not as high between 
one and two or between two and three. So I would 
conclude that once the children are three months old, they 
are pretty much fixed in a high or a low category. Before 
that they can still change from a high to low category. It is 
not because they got their HepB in the first month that they 
will also get the other ones in the following months. 


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Dr. Rennels: 


Dr. Verstraeten: 


Dr. Gerber: 


Dr, Verstraeten: 


But in fact by four months they may all be in the same 
category? 

They could be, but they are not. One of the main 
differences is the hepatitis B. Whether they got it or not, 
and those are already three doses. That’s 37.5. Although 
you are right. There is pretty much two peaks and the 
difference between the two is the hepatitis B. Besides that, 
there is not such a wide distribution. 

I wanted to get back to the issue of medical care utilization 
as a possible confounder. You told us about a week ago 
that you were beginning to see some differences in the 
second year in one of the HMOs in terms of the number of 
office visits. Could you elaborate on that? 

Let me show you a couple of slides on what we have been 
trying to do. The first thing I looked at is the number of 
visits these children have in the first year of life during the 
exposure time. I have divided them into two different 
types of visits. Just a well child clinic which has specific 
ICD9 codes, or any other visit including those well child 
clinics. These are categories at three months of age. Then 
I have looked at the different categories of exposure to 
have an idea if there is a difference or not between the 
number of visits these children have and the different 
exposure levels. What this suggests is that as you go up the 
exposure levels, the number of well child visits increases, 
which is not really surprising because most of the 
vaccinations are given during those visits. However, 
although not perfectly, but there is also a suggestion of an 
increase, although it goes down here and then back up for 
the overall number of visits, so that is including a visit for 
any problem the child has. 

However, when I adjust for these numbers, if I put this in 
the model as a co-variable or as stratified on it, it doesn’t 


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Dr. Stehr-Green: 


Dr. Verstraeten: 
Dr. Stehr-Green: 


Dr. Verstraeten: 


Dr. Walker: 


Dr. Verstraeten: 


Dr. Walker: 


change the estimates anyhow. It doesn’t seem to make 
much difference. 

Essentially it seems to me you may be calling the same 
variable, or the same characteristic two different variables. 

That’s possible. 

One thing I thought you might do is if these kids have 
siblings, you might take the average number of visits the 
sibling had and you could use that as a covariant. It can 
still be correlated with the visits that your study subject 
had, but it is not going to be calling the same characteristic 
two different things. There may also be other ways. 

Somebody has mentioned that before, what about siblings. 
We could look at that, but unfortunately I don’t think we 
have the means in our automated data to find out who is the 
sibling of who, so that wouldn’t be possible using the 
automated data, but that’s definitely a great idea. 

I’m troubled by this table. What you are telling us is the 
average child in these HMOs has 12 visits in his first year 
of life? Or 10 to 12. That number just seems a little large 
to me for an average number, and I am wondering what 
you are counting as a visit and that leads me also to ask you 
what you are counting as a diagnosis? I know these aren’t 
claims databases, so it’s not the diagnosis associated with 
every test. 

You are right, these are diagnoses, they are not visits. I’m 
sorry. These are visits. Unless they give them twice at the 
same visit, these are diagnoses. 

So these are new diagnostic codes entered for a child, so a 
child could have multiple at one visit? 


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Dr. Stehr-Green: 


But those could include administration of vaccines, right? 


Dr. Walker: 


Dr. Davis: 


Dr. Walker: 


Dr. Davis: 
Dr. Walker: 


Dr. Davis: 


Dr. Walker: 


Let me finish, please. So can you tell us the circumstances 
under which a code comes into the file? And you’re 
counting it as an outcome. Maybe that’s specific to each of 
the two HMOs. 

First I would argue that this is probably normal. Even if 
they are visits, I would actually disagree with that this is 
above, because number one, you get your discharge from 
the hospital. You get your two week visit. You get your 
two, four and six month visit. Your nine month visit and 
your 12 month visit. Then you get your three colds in the 
first year of life. I think that’s 1 1 . 

Well, that comes out to more than two by one month of life 
and you’re averaging less than two. 

I’m sorry, say that again. 

The question is what do these codes correspond to? Are 
they a code given at the time of a visit with a health care 
practitioner or can these codes appear in any other context 
and still get into your file? 

Yes, if they see an emergency room physician and I think 
for telephone calls we have some text strings. I don’t think 
they get coded, so I think it’s actually medical care 
utilization. They tend to be check box, so people would 
check boxes and then that gets coded in a different manner 
I might say, so that’s how the diagnosis itself makes it into 
the automated file. 

Now there are very few of these diagnoses which would 
actually result in only a single encounter and never again 
be the case of medical care. I would think that you could 


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Dr. Verstraeten: 
Dr. Walker: 

Dr. Verstraeten: 

Dr. Chen: 

Dr. Verstraeten: 
Dr. Chen: 

Dr. Verstraeten: 


Dr. Walker: 

Dr. Verstraeten: 


get a lot of noise out of this system by looking at people 
who have had at least two visits with a particular code. 

You are talking about particular outcomes? 

Yes. 

That is something we will talk about later. Yes, we have 
done that. 

Why don’t we show that slide now? 

Where they have been diagnosed more than once? 

On repeat visits for the same... 

But I am not through with this medical care utilization, we 
will come back to that. 

Now, for some of the outcomes, how many of these were 
diagnosed more than once? Autism, 40%, and there is a 
difference between the two HMOs. Speech delay, 37% and 
here it is higher at Group Health than at Northern 
California Kaiser. Attention deficit disorder, again the 
other way around, but they are pretty much in the same 
ballpark. The proportion of the cases in which the outcome 
has been diagnosed more than one. I think that was your 
question, correct? 

Yes, have you done the analysis for each case? 

Yes. It comes back on the discussion also this afternoon of 
the chart abstraction. For attention deficit disorder, that is 
the same estimate except that confidence intervals become 
wider. The number goes down. For speech delay, actually 
the estimate slightly increases. This is at three months of 
age, so this compares to the 1008. The level of 


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significance, I’m not sure how that is affected, but basically 
it’s pretty much the same thing. And I can tell you, 
although 1 don’t have the figures in here, that for Tics there 
are too few, so I couldn’t tell you. For language delay it’s 
the same thing and for unspecified there are also too few 
because I think there are very few that come back twice. 
But basically for the ones where it was possible to do this, 
it was confirmed. 

Dr. Walker: Well, no, there is only two categories now in which you 

have enough data. That doesn’t imply that the others are 
good. 

I’m saying for the ones where it was possible to do this 
analysis, it would confirm what we saw. On top of that, I 
could go up twice, three, four, five and it would just 
increase the estimates basically, and that was only at Group 
Health. 

Now going back to this medical care utilization, now I am 
looking at the number of visits. Just plain, the number of 
visits. Not just the first year of life, because another 
concern is that maybe the children of higher exposure come 
back more regularly later and have a higher chance of 
being captured later on. So what I have tried to do instead 
of giving you just these numbers, I have made just plain 
linear regression models of the exposure and the number of 
visits, to see if there was a linear correlation between the 
'***' two. 

What we see is that at Group Health, that appears to be the 
case. I have divided it by years of birth because I think it is 
important to keep in mind that there are these temporal 
trends. So for the different years of birth, at Group Health 
there is this trend. Really that the children with higher 
exposure are more likely to come back. 


Dr. Verstraeten: 




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Now we get into the problem of mixing outcomes and 
confounding variables, because do they come back because 
they are sick or do they become sick because they come 
back many times? That becomes hard to differentiate. 

At Northern California Kaiser, that trend is hardly there. It 
varies more around zero or it can even be negative for one 
year. 

I don’t think we should look at the significance of these 
numbers, but they just suggest that the trend is there. 

This is the same, but just for well child visits and we see 
the same thing. That at Group Health there is a trend, that 
higher exposure groups have more well child visits. AT 
NCK that is not apparent. 

These estimates are now using the number of well child 
visits in my proportional hazard models instead of the 
mercury exposure, and we see that for both ADD and 
speech delay, those two are significantly linked. So the 
more well child visits, the more likely to be diagnosed. 

This is again looking at the mercury, but adjusting for the 
number of well child visits, and it doesn’t affect the 
estimates. But again we have the problem we had before, 
that some of these variables now may be correlated and it’s 
not obvious how that affects our estimates. 

I hope this makes sense. Trying to adjust for these number 
of visits, but if this is very correlated to the exposure, that’s 
not obvious if we can just do that. Anyway, we went ahead 
and did it and it doesn’t really affect the estimates. 

Dr. Oakes: So that correlation will be taken into account in your 

confidence intervals? 


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* 



Dr. Verstraeten: 


I’m not sure I want to say something about that. 


Dr. Walker: 


Dr. Verstraeten: 


Dr. Oakes: 


Dr. Modlin: 


Dr. Verstraeten: 


Correlations which are you are accounting for when you 
control for confounding, so the fact of correlation is not by 
itself destructive of this. 

But at the same time if there is correlation, you may not be 
surprised that it doesn’t affect your estimates. 

Well, it is true that if there is measurement error in either or 
both of these, which there almost certainly is, then it 
becomes less clear cut. 

If you could go back and have people take a look at slide 
11 in your original presentation. My question... 

Before that, can I just finish up with the medical care 
utilization and then we’ll get to that? Just to avoid jumping 
back and forth, if I can have the next slide on medical care 
utilization. 

Something else we have tried to do, because we are 
thinking medical care utilization could be a link to 
socioeconomic status, and that could be another fact that is 
behind this, we have linked our data to 1990 census date, 
and then trying to assign race and income to the children. 
That is information that we don’t have in our automated 
data, but we have been trying to do that by linking this. If 
we do that, we see this would be the racial distribution of 
our cohorts with the majority being white and then the 
second group would be Hispanic, followed by Asians and 
then blacks and a very few native Indians. 

What I looked at here is what is the mean cumulative 
exposure at three months for these different racial groups, 
and they don’t differ very much. The one that is different 
is the native Indians, but there was only three in this 


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category. So amongst the others, there is not much 
difference. 

The outcome, however, can be linked. I saw that among 
the white group, they were more likely to have some of the 
outcomes of neurodevelopmental delays, which also is 
maybe not surprising. However, when I put these racial 
groups and stratify on it, it doesn’t affect the estimates. 

Also if I look at the estimates only within this group, it is 
also very close to the original estimate. 

The next slide shows income. This would be household 
income and I’ve categorized them as follows. Between 
$15,000, et cetera. Again, the mean cumulative mercury 
exposure does not differ between these groups, however, 
there is one group that is predominating the whole cohort. 
And again when I stratify on these groups, it doesn’t affect 
the estimates, at least for the significant findings. 

That is all for medical care utilization. We could return to 
your question. 

Dr. Chen: John, before we get to that. One way though to look at 

whether medical care utilization might be a potential 
confounder would be to look at other outcomes other than 
renal and neurological to see if we see the same kind of 
consistent trends. That might be useful before we jump to 
the other topic. 

Dr. Verstraeten: One other thing I did, what would happen if I just look at a 

few other outcomes that I don’t think are related to 
Thimerosal. Am I going to see the same kind of trends? 
Maybe there is something in the data that I am not 
understanding. So I have selected a number of outcomes. 
First of all I have selected three outcomes among the most 
frequent outcomes, unspecified conjunctivitis, non- 


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Dr. Stehr-Green: 

Dr. Chen: 

Dr. Stehr-Green: 


infectious gastroenteritis and unspecified injury. These are 
the number of cases for these three outcomes. 
Unfortunately, I don’t have the mean age. I haven’t had 
the time to redo this and I hadn’t written it down originally, 
because I think it would be important to better understand 
whether we can compare these outcomes. On top of that, 
we have selected two outcomes that we thought would be 
similar, also prone to the bias that the effect that the child 
has received that diagnosis somehow reflects parental 
concern. That not any child with these will be taken to a 
doctor. There is one code which is called worried well, 
which specifically states that the parent came with the child 
for a problem and the doctor said there was not problem. 

The next one is flat feet, where we assumed there was a 
certain degree of parental concern needed to bring a child 
to the doctor for flat feet. 

This is the graph for conjunctivitis. The same type of 
graph with the exposure categorized at three months. What 
happens is that here the zero group has a lower risk. It 
appears as if this group is just not being vaccinated and are 
not coming to the HMO. After that it is pretty much a 
straight line. Nothing much happens here once the child 
gets any vaccine. 

They are all elevated compared to the reference category, 
but the trend is right here. Although that is significantly 
above one, that is a . 1 risk. This is about only one-tenth of 
what we saw in the others. 

Ironically you didn’t show any of your Thimerosal related 
outcomes. Every exposure level above zero was skipped. 

Because the ends are bigger. 

Good point. 


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Dr. Oakes: 


Dr. Walker: 


Dr. Davis: 


Dr. Verstraeten: 


Dr. Stehr-Green: 


Dr. Verstraeten: 


Dr. White: 


But the point you are making is that there is an artifact in 
the blood category there. 

That’s one thing. This zero group has a problem. I think 
this zero group is a mixture of children. Either they are too 
sick to be vaccinated and that is a problem that happens 
with the prematures, or they just don’t come to the HMO. 
They just get their vaccines somewhere else and then they 
are also not diagnosed. 

I might be very dense here, but they do get two polio 
vaccines. 

Before the end of the first year. That’s true. When we 
look at non-infectious diarrhea it is the same story. It goes 
up compared to the reference category, but after that there 
is hardly any trend and the test for trend gives the same 
result as the previous graph. 

Now in a way this one is also interesting. This is for injury 
where actually the trend now is down. There is a 
significant downward trend suggesting the more 
Thimerosal, the less likely to be injured. If one would try 
to explain this, is that the same parents who are concerned 
about having their children vaccinated are also concerned 
that their children don’t get injured. That they are more 
careful. 

Again, point estimates suggest that none of those are 
significantly different from zero, so I think that’s a rather 
spurious conclusion. 

This graph is up and down. It doesn’t suggest the same 
trend. 

Do we have vaccination rates for each one of these things? 
Aren’t they greater than 90%? You showed to begin with 


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Dr. Verstraeten: 
Dr. Davis: 

Dr. White: 

Dr. Modlin: 


the vaccination rate of all the children in the age modes is 
greater than 90%, isn’t it? You had included the 
vaccination rates of who gets vaccinated. Was it low or 
was it high? 

I don’t know what the vaccination rate is. 

Since that is tremendous over time. Maybe Ned, do you 
know the answer for NCK? At Group Health it was about 
74% coverage by two years of age for a whole definition of 
what we are using today, but it went up to 91% very 
quickly. Like within two or three years after that. I don’t 
think that is getting to what you were asking though. 

I wanted to know if you were looking at these are parents 
who are giving their children these vaccines or not? I don’t 
know. 

Maybe we could explore this question a little further 
because I think it is important. A couple of questions have 
come up. Actually Peggy’s original question about 
exposure at six months of age also raises the same issue. 
That is if you look at your distribution of exposure that you 
showed in slide 1 1 on your original presentation, showing 
the frequency of exposure for numbers of each of the 
individual categories, you’ve got almost 2,500 kids that had 
no exposure. Zero exposure to mercury. About an equal 
number in the other two lower exposure groups. This is at 
three months of age at 12.5 and 25. Then of course your 
numbers go up by a factor of 1 0 or greater. 

The comparisons here are critical because the zero 
exposure group is actually your comparison group, and 
since you’re seeing trends in the data and it appears to be 
the trends that are bothering us the most, when you are 
comparing data in the higher exposure groups to the lower 


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exposure groups, these lower exposure groups at the 
relatively small numbers become very, very important. 


Dr. Verstraeten: 


Dr. Walker: 


Dr. Verstraeten: 


My question is what is it about kids who get no 
Thimerosal, but still get two does of polio vaccine by a 
year of age that’s different from kids who get exposure to 
the usual numbers of doses that we would expect if they are 
fully immunized by three months of age? My guess would 
be that these kids who are getting the lowest exposures are 
kids who are being immunized late. That’s the only way in 
which they could get in the study. If they are getting their 
polio vaccines at the same time they are getting their DTP 
vaccine. So they are being immunized, and it may not be 
just the zero exposure kids. It may be those in the lower 
exposure groups as well who might fit into that category as 
well. So there is something different about them. That 
difference is probably very important. 

Let me show you some graphs. I looked at these kids at 
one year of age. How many of them were on time for their 
vaccinations or not, so let me show you. 

While he is getting that out, the trend statistic isn’t really 
being driven by that low dose group. You’ve have to look 
down to where the numbers are, so when he gives you a 
trend statistic it is really mostly averaged over the 37.5 to 
the 75, and the very dose ones are weighted more heavily 
because they are extreme, but still the numbers are so 
small. They are not much of the estimate. 

That is the other comment I wanted to make. When we 
have the trend estimate, we don’t have a reference 
category. 

Originally I had taken the high group as a reference 
category, but the first time I showed those results people 
were always trying to revert from below one to above one, 


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Dr. Oakes: 


Dr. Verstraeten: 


and that was so confusing, but then the graphs would go 
down. It was pretty much the same results, but then I 
decided to stick to these trend tests which I think are less 
bias because they are not fixed on one reference category. 

But if we can look at this graph, what I looked at is that 
among these different categories at three months of age, 
how many kids end up being on time by one year of age? 
The end of the first year? So they would have their 
required number of DTP, HIB and polio, excluding 
hepatitis B here. What we see is that once they are at 37.5, 
almost all of them are vaccinated on time. The ones below 
these three categories, they are still about 50% and 
strangely enough this one was even lower. There are still 
about 50% that get their vaccines on time. There is another 
50% that doesn’t get them on time and this is the one 
probably to worry about. 

This is the same thing including hepatitis B, and not 
surprisingly those figures are increased a bit where there 
are a higher number of children who do not get their 
hepatitis B by the end of the first year of life, or don’t get 
their entire vaccination schedule by the end of their first 
year of life. 

I’m missing something here. If they are getting their 
vaccinations on time in the upper part of this, why is there 
any difference in the exposures at all? 

There are several possibilities. In this they get their 
vaccinations on time by the end of the first year of life, so 
they might have gotten it, not before three months, but after 
three months. Then there is also a difference between 
DTP-HIB combined or separate. That makes a difference 
of 25 micrograms. That is something Phil will talk more 
about this afternoon. 


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Dr. Stehr-Green: 


Dr. Verstraeten: 


Dr. Stehr-Green: 


Dr. Modlin: 


Dr. Johnson: 


Dr. Walker: 


Dr. Verstraeten: 


This brings my level of concern even higher. It may not be 
an issue of confounding we are dealing with, it may be an 
issue of bias. Whereas these kids who aren’t getting 
vaccinated in the first three months of life, they are just 
essentially dropping out. So not only are they not getting 
exposed to Thimerosal, they are not getting an opportunity 
to be diagnosed with any of these other outcomes. Yet they 
are still in the cohort because they make their entry criteria 
of having two polio vaccines, but they are not having 
enough visits to get either vaccinated and therefore 
exposed, or to be seen and get diagnosed. So it seems to 
me it may not be an issue of confounding, but we have to 
think about an issue of ascertainment bias. 

It’s possible, but we are also not sure. We don’t know why 
these children don’t have visits. Maybe they could come, 
but they don’t come for some reason. 

But the question of why may be irrelevant. I’m saying that 
may be what is driving some of your observations. 

They have visits, they are just delayed. They are getting 
visits because they are getting their two doses of polio later 
on and ultimately becoming fully immunized. 

But how would that explain the alternative diagnoses? The 
trends we see there. That explanation would have to apply 
to both the mercury plausible outcomes as well as those 
alternative. 

You could address the criticism pretty easily and without 
much cost by simply truncating your lowest exposure 
levels since you don’t have very many people anyway, and 
taking as your reference group a typical. 

Right, that is something Phil will talk about this afternoon. 


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Dr. Mawle: 


Dr. Verstraeten: 


Dr. Mawle: 


I’m still on this same graph, with your different levels of 
exposure between 12.5 and 50, you have two different 
ways of getting there. You can either get two doses of 
hepatitis B, which presumably would occur at two different 
levels, or you can get them all in one. Now one of the 
problems you have with Thimerosal is you don’t know 
what that does to the actual blood levels of the body blood, 
but presumably if you’ve got them spaced it would be 
different than if you got them all at once. Did you analyze 
them separately? 

I know, that raises the other issue. Exactly like what 
you’re saying, the timing of when they get this may also be 
important and maybe this comparison is not perfect 
because some of them got it at one month or two months, 
and it’s pretty hard. What I have tried to do is like stratify 
on what they got before that, but then you start mixing up 
things. It becomes quite confusing. 

Another possibility is giving it different weights depending 
on when they get it and the later after birth, the less weight 
you give it, et cetera. There is different ways to go about it, 
but I think at a certain point it becomes a bit too complex 
or a bit too confusing, although you can still try to do that. 
But to sort of understand what’s going on, it gets a bit too 
mixed up. In a way it’s possible to do that, but the 
variation decreases a lot, too, if you start doing that and if 
you start stratifying about what happened before, about 
what happens afterwards, you loose. 

I was concerned. There’s a big difference between getting 
them all at once and getting them spaced presumably at 
least a month apart, and when you look at the levels we’re 
talking about, which is a chronic exposure versus the acute 
exposure, those three categories are not comparable. At 
least they’re presumably not comparable. And I think that 
the NIH studies are supposed to be addressing some of 


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Dr. Guess: 




Dr. Verstraeten: 


those issues, but I don’t think that at this point we can truly 
say that all of your 37.5 for instance are equivalent. 

I’m not entirely convinced by the analyses showing no 
trend in these other diagnoses because for example, the 
gastroenteritis and the conjunctivitis would be things that 
you would think the parents would probably bring children 
in for, whereas some of these developmental things, 
particularly the more subtle ones, may not be. In other 
words, a profound developmental, yes, but a subtle one 
perhaps less so. So it would seem, to me anyway, that to 
rule out the issue of the ascertainment bias, one might need 
to examine other kinds of diagnoses not thought to be 
associated with Thimerosal and which may be things that 
parents may not bring people in for. So I think it’s a good 
line of reasoning, but I’m not sure it’s been entirely put to 
rest. 

Could I have slide 32, I think that addresses that question. 
I’m sorry, 31. I mentioned these other two diagnoses, the 
flat feet and the worried well. I haven’t showed you the 
results for those. What we have for those two are the 
estimates. For the worried well and the flat feet, both of 
them are non-significantly different from one. They are 
both below one. The finding is not significant. That’s at 
one month and at three months of age. 

The last category, maybe I will talk about this now because 
it’s good to be aware of this. There is analysis we have 
done where we compare the children that got DTP-HIB in 
the combined vaccine or DTP-HIB separate, which is a 
difference in exposure of 25 micrograms. But basically we 
assume that these children are comparable. They get the 
same number of antigens. They get the same number of 
vaccines. They come pretty much at the same time for 
vaccinations, except that one gets the vaccine in one shot 
and gets 25 micrograms less than the other children. So 


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Dr. White: 

Dr. Verstraeten: 

Dr. Myers: 

Dr. Verstraeten: 


when we do that, at least for these outcomes, we see that 
it’s all rather centered around one and none of this is 
significant. 

If we do that for the other outcomes... 

Would that make it confounded by different HMOs? You 
were talking about that back here where the HIB combined 
was used in one HMO. 

Right, this analysis is limited to one HMO, not the other 
one. Absolutely. Anyway, I think that was something in 
the text that you have received. Does DTP-HIB combine 
on separate analysis? The original test that was handed to 
the people. 

The one they got in the mail? 

Yeah, the one they got in the mail. Basically what we 
found when we do that is that for most of the outcomes, or 
for all of the outcomes, none of the estimates are 
significant. Most of them are above one, but none of the 
findings are significant. However, the power of this 
analysis is limited because it’s basically only in one 
calendar year that it happens. That vaccines were given. 
Some kids got the combination vaccine or some kids got 
the vaccines separate. 

However, among prematures, that becomes significant and 
we get relative risks up to two and three, whereby the ones 
who got more Thimerosal are at higher risk than the ones 
who got the combination vaccine, so about 25 micrograms 
less than Thimerosal. However, the number of children in 
this analysis is quite small and that result is quite sensitive 
to small numbers. 


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Dr. Snyder: 


Mr. Verstraeten: 
Dr. Davis: 


Dr. Rhodes: 


This issue of ascertainment biases is obviously something 
of great concern. With regard to that there is a piece of 
data I haven’t seen yet that you may have looked at which 
has to do with looking at the proportion of children by level 
of mercury exposure who remained in the HMO at 18 
months or two years or three years. At points in time at 
which the cases would be ascertained. If there is no 
difference in the proportion, it gives you a higher level of 
confidence that there is something there, whereas if they 
don’t remain in the HMO it just exacerbates the concern 
about ascertainment. Is that analysis condoned? 

That has no meaning. 

The analysis as it is set up takes that into account though 
because people are censored at the point that they drop out 
of the analysis, so basically at any given age in their life, 
let’s say at two years of age, you are comparing people 
who are put on the analysis based on their exposure 
category, then they are followed up for the outcome and 
then censored when they drop out. So we are not really 
concerned about people who disenroll from the HMO. 

I think maybe what I should do is just suggest to restate to 
your concern. They are sort of dropping out from health 
care seeking behavior, but remaining in the HMO. Maybe 
that’s what you are getting at. 

The point that Dr. Davis makes about censoring is fine 
except people who are at higher or lower risk are more 
likely to be censored, then it’s still a problem. 

It does turn out that the kids who are in the low group at 
GHC actually leave the HMO faster than kids who were in 
the higher groups, at least in terms of disenrollment dates. 
Now as to what they’re doing in terms of their medical care 
before that, that’s not in question. 


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Dr. Stehr-Green: 


Dr. Verstraeten: 


Dr. Johnson: 


Dr. Verstraeten: 


Dr. Stehr-Green: 


With regard to ascertainment bias from a general 
pediatrician’s point of view, the outcomes that have been 
produced by this study, a neurodevelopmental and a 
neurobehavioral outcome in children no older than five or 
six years, can be very dependent upon the concern of the 
parents. Particularly speed delay. There are parents that 
will tolerate tremendous variations in speed and language 
in the first three to four years and pediatricians rarely see 
children or evaluate children speaking in their office to the 
extent that they can make that diagnosis. So I think you 
have a real bias in the interest of a parent to make this 
diagnosis, and how you can use that in comparison to 
Thimerosal levels, 1 don’t know. But I think it impacts on 
your conclusion tremendously. 

I agree. That’s the main bias we have a problem with. The 
only remark I would like to make is that we always 
assumed that concerned parents would also have their 
children more vaccinated. I am not sure if that is 
something you can just assume, but that’s the underlying 
assumption that we are making. 

There are a lot of questions remaining and I think we’ll 
have to decide during the lunch period how to deal with 
those. If we do not break now, we risk not having any 
lunch at all, so we have to start with that. We’ll be back at 
two o’clock. 

Thank you Tom and also Bob Chen so deftly managing the 
slides. 

I did manage to find the slide I couldn’t find before lunch, 
we I’ll start the afternoon session with that one. 

If you can take your seats in the back please, as we are 
limited for time. 


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Dr. Verstraeten: 


Dr. Rodewald: 
Dr. Verstraeten: 


Dr. Rhodes: 


Dr. Verstraeten: 


Dr. Rhodes: 


These are the risk estimates by comparing the DTP-HIB 
separate to combined, which is a difference of 25 
micrograms of ethylmercury and the combined with the 
lower mercury content is the reference group and these are 
the findings. 

As I mentioned before, almost all of them except for this 
one are above one, however, none of them is statistically 
significant. 

What kind of end are you talking about? How much... 

It would be about between one-third and a half. I 
apologize, I didn’t put that, but it’s anywhere between one- 
third and a half off the total sample size. So say for speech 
that would be about 500, more or less. 

Tom, if you look at it, is this limited to Northern 
California? 

Absolutely, because in Group Health they didn’t use the 
combined vaccine, so it’s only Northern California. 

The ends here can be very confusing because of the way 
the models have been fit in a very stratified fashion based 
on month of birth. 

The switch over from a separate DTP-HIB to a combined 
HIB at NCK was done very quickly over the course of a 
couple of months, or at least as it appears in the data. So 
that when you stratify by month of birth, you essentially 
throw away all those kids that occur before the switch and 
all those kids that occur after the switch has been 
completed, although as has been said, there are some 
possible miscodings in kids who appear to have the wrong 
version after the switch. So that there are sort of two 
problems here. One is that you may start with say 1,000 


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Dr. Sinks: 

Dr. Verstraeten: 


Dr. Guess: 


Dr. Verstraeten: 

Dr. Guess: 

Dr. Brent: 


cases, but if only 100 of those occurred during the switch, 
you’re working with 100 cases and not 1,000, and if cases 
occur after the switch, but they are miscoded, then they will 
inappropriately enter into the analysis. 

Just interpret the relative risk again for me. Is that again 
using the model of one microgram, a unit change of one 
microgram or what? 

No, this is the relative risk as you are used to seeing it. If 
you are in one group compared to any other group, then 
your risk is say for 313, it’s 1.5 and it’s not by micrograms 
of mercury, no. 

Yes, the difference is 25 micrograms, but it’s not divided 
by the micro... yes please. 

I just wanted to clarify the question that Phil raised. Am I 
to infer then that the sample size is restricted to the children 
who were getting both vaccines during the time period 
when both were being used, and it doesn’t include the 
people when it was all combined or all non-combined? Am 
I understanding that correctly? 

Well, actually it was including all of them, but the way the 
model works, the way that it’s stratified on month of birth, 
there isn’t much that can be compared in any of the other 
months, so those weren’t, wasn’t very much to them. 

Okay, I understand. Yes, thanks. 

I would like to go back to the design with regard to the 
pharmacokinetics. The fact is that in the introduction it 
really is unclear as to whether this is a water soluble form 
or whether it’s organic. In other words they say that. I 
don’t know whether it’s on to the other, but the point is that 
if you administer these doses over the period of an interval 


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of weeks, we don’t have any evidence that the level is 
rising. In other words, if the exposure is changing. If the 
half live is like two weeks or 14 days or 18 days, by the 
time they get the next injection you’re back to the 
background level. So that the whole idea that you have 
successive increases in exposure may not be true. The fact 
is that six months the blood level of mercury may be 
exactly the same as it was after the first dose. So that all 
those calculations of adding up the doses, if you have 62.5 
micrograms of mercury, may not be true. 

The second thing is, when you talk about neuro-behavioral 
effects, you’re talking about what we all deterministic 
effects. They are thresholds. I don’t know about 
ethylmercury, but methylmercury, the threshold for neuro- 
behavioral effects in like the rhesus monkey and in many 
animal species is way above the exposures that these 
infants are receiving. It’s in milligrams per kilogram, not 
micrograms per kilogram. So all these levels, whether 
there is a dose response curve or not, may be below the 
threshold for producing any neuro-behavioral effects. So I 
think it would be very important to get the 
pharmacokinetics out of the way to find out what are the 
blood levels or the tissue levels of the ethylmercury in the 
infants over this six months period. 

You know, all these calculations, statistics and re-analysis, 
if it’s not based on firm pharmacokinetic exposures is not 
^ very easy to interpret. 

Dr. Verstraeten: Thank you. I think to answer the first part, what we have 

been saying is that this is the cumulative amount that these 
children have received. That does mean that at three 
months that would be related to their blood levels or hair 
levels or whatever. That this accumulates in the blood. 
That’s not what we’ve been saying. 


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Dr. Brent: 


Dr. Verstraeten: 


Dr. Weil: 


Dr. Brent: 


All we have been saying is that this is the amount they 
received. We know that’s true. If that amount is 
accumulated in the tissue or in the blood, that we are not 
aware of. And as you are saying, as long as we don’t have 
the pharmacokinetics at mercury, there is no way we can 
assume one way or the other. So we can only work with 
what we have, which is the amount that they have received. 

Because the most important thing with the biological effect 
is the dose that the central nervous system or the 
developing cells are receiving. If you never raise the dose 
and the dose is always below the threshold, then you don’t 
have a biological effect even possible. 

Right, but at this point there is nothing we can say about 
the actual dose. 

Bob, you are assuming a threshold. The hypothesis here 
sounds like it’s an exposure dependent related, dose 
related, and you don’t know what is below the threshold 
you are referring to, which was an animal derived one. 

There are two kinds of effects from a so-called 
toxicological viewpoint. One is called a stochastic effect, 
where the dose goes to zero. In other words there is no 
dose that presents no risk. And the second is the so-called 
toxicological S-shaped curve where the dose is S-shaped, 
and when you get down to a certain level the effect is no 
different than the controls. 

The only diseases that have a stochastic effect where the 
dose goes to zero are those diseases that can produce by 
changing a single cell, and those are malignancies and 
genetic disease. Those are the only two diseases that have 
a risk from let’s say a mutagenic exposure that goes to 
zero. Everything else has a threshold because it is a multi- 
cellular phenomenon. You cannot produce learning 


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Dr. Verstraeten: 


Dr. Weil: 


Dr. Brent: 


disability by changing one neuron developing, or autism by 
changing one cell in the cerebral cortex. It’s a multi- 
cellular phenomenon. Therefore it has to have a threshold. 

I don’t know what that threshold is, but based on the 
methyimercury data it is far above any dose that we are 
presenting to infants in these studies. 

Two issues. First of all, like you say the threshold is 
established from methyimercury. I think we should avoid a 
discussion of how do we compare methyimercury to 
ethylmercury. I think that would take us very far. 

Secondly, we are talking about biologic plausibility, and I 
would ask that we reserve that for later on when we have 
the appropriate time to discuss those issues. 

I think what you are saying is in terms of chronic exposure. 
I think the other alternative scenario is that this is repeated 
acute exposures, and like many repeated acute exposures, if 
you consider a dose of 25 micrograms on one day, then you 
are above threshold. At least we think you are, and then 
you do that over and over to a series of neurons where the 
toxic effect may be the same set of neurons or the same set 
of neurologic processes, it is conceivable that the more 
mercury you get, the more effect you are going to get. 

For every dose you give, it’s gonna get above the threshold, 
because what it is, below the threshold the recuperative 
powers of the tissue enables not to respond in a negative 
way. You have to be careful if you keep forgetting about 
the importance of dose. I don’t care whether you give it 
one time or four injections over a period of six months. It’s 
whether the level below the dose that affects the 
development of the central nervous system, they’re not 
going to have an effect. 


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Dr. Verstraeten: 

Excuse me, I understand all this, we’ve only got 1 5 more 
minutes to have the discussion. 

Dr. Johnson: 

Yes, let’s hold this. Just put it aside for a while and we’ll 
come back to it. 

Dr. Verstraeten: 

There is just a number of slides I would like to show 
because I think they have their own importance. 


Next slide, please. I mentioned at a point that it’s very hard 
for us to differentiate whether it is Thimerosal effect or 
anything else. 


What I have done here, I am put into the model instead of 
mercury, a number of antigens that the children received, 
and what do we get? Not surprisingly, we get very similar 
estimates as what we got for Thimerosal because every 
vaccine put in the equation has Thimerosal. So for speech 
and the other ones maybe it’s not so significant, but for the 
overall group it is also significant. So that is very difficult 
to distinguish. 


Here we have the same thing, but instead of number of 
antigens, number of shots. Just the number of vaccinations 
given to a child, which is also for nearly all of them 
significantly related. 

Dr. Myers: 

Tom, just on the number of antigens, did you add in the 
other antigens that were dropped at the beginning? 

Dr. Verstraeten: 

Yes, I added polio which was basically the one that was 
missing. It doesn’t change, no. 

Dr. Guess: 

What are the units here? 

Dr. Verstraeten: 

The number of antigens. 


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Dr. Guess: 

Dr. Verstraeten: 
Dr. Egan: 

Dr. Verstraeten: 


Dr. Egan: 

Dr. Verstraeten: 

Dr. Egan: 

Dr. Verstraeten: 
Dr. Sinks: 


So this essentially in a 7% risk per antigen, and an antigen 
is like in DPT you’ve got three antigens. 

Correct. 

Could you do this calculation for aluminum? 

I did it for aluminum. Actually that was the last thing I did 
last night before I left the office. I just did it for NCK 
because for Group Health it would have been more difficult 
to program. Actually the results were almost identical to 
ethylmercury because the amount of aluminum goes along 
almost exactly with the mercury one. There is one vaccine, 
HibTITER, that doesn’t have aluminum, but then if they 
get a HibTITER, they get a DTP and the DTP has 
aluminum. So they are almost identical. 

You were doing these as the number of antigens, not as the 
number of shots? Because the more shots, the more 
Thimerosal. 

Yes, I did both, number of antigens, number of shots. The 
first slide was the number of antigens, the second was the 
number of shots. 

So in other words, some of the children are missing their 
vaccines then? Or at least for that time period. 

Yes, which is the same as before. 

Absolutely there is a lot of correlation or co-linearity 
between this analysis and your primary analysis with 
mercury, but in terms of evaluating the confounding, it 
would be nice to see what happens with the risk estimate in 
the model that’s showing these things, so you can actually 
see is it blowing up on you. What is actually happening. 
How co-linear are they. 


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Mr. Verstraeten: 
Dr. Sinks: 

Dr. Verstraeten: 


It is not surprising at all that we are seeing this. The size of 
the relative risk is obviously different because you are 
looking at different units and you can’t compare one 
microgram of mercury versus one antigen. But it would be 
nice to see in a model both of these values, the relative risk 
at the same time. 

My guess is that what is happening, I wouldn’t expect both 
of them to remain statistically significant. 

You mean if I put both at the same time? I didn’t try that. 

Oh, okay, you didn’t have them in the same model. 

No, these are separate. Absolutely. No, I just showed this 
to illustrate that with this data it is pretty impossible to 
differentiate. 

The only option we have is the DTP-HIB combined or 
separate. That is the only one where the aluminum is 
identical, the number of antigens is identical. Only 
mercury is different at that point. 

Then the last slide I wanted to show, there was a question 
of if there was any way from this data that we could 
estimate what would happen in the future if there is 
Thimerosal-free HepB and Thimerosal-free haemophilus 
influenza vaccine and only DTP has Thimerosal. 

What I tried to do is I took out of the cohort those children 
that increased their Thimerosal amounts by 25 micrograms 
between one and three months of age, which is when they 
have already received the HepB and when they have 
received their DTP and Hib. Those are the estimates right 
here. So those are the children that between one and three 
months of age, they have increased their mercury amount 


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by 25 micrograms, which is what would happen if DTP 
would be the only vaccine with Thimerosal. 

None of these estimates are significant, however, the 
sample size has gone down quite a bit. Fm sorry, I don’t 
have the numbers here but they are around 100 to 200. 
They are not very high. 

The second column would be the same scenario, but now at 
six months. Assuming they have received two additional 
DTPs, so between three and six months of age they have 
increased their ethylmercury amounts by 50 micrograms. 
If I do in this current cohort with all its limitations, because 
there is also the HepB that exists in this cohort, I can’t 
really take it out. It is significant for this one disorder 
which is language delay and it is quite high. Together with 
that, speech or language delay which is a combination of 
these two disorders, also becomes significant. 

The overall group is borderline, not significant. Basically 
what one could say, if you can assume that this is a valid 
analysis, it doesn’t give you complete security. I mean 
there is still a problem at this level. 

Dr. Staub: I am a little confused. In this analysis these children would 

not have received a hepatitis-B at birth dose, is that 
correct? 

Dr. Verstraeten: They can have received. I have done it irrespectively of 

whether or not they have received that. 

Dr. Staub: So I guess I will ask my question that I talked to you about 

before lunch, which was in the pre-reads that you sent us 
there was a table which had your statistically significant 
results in it, and language and speech delay were 
significant at one month of age and essentially carried that 
significance through the rest of the analyses. 


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And then your graph that shows the relative risk increasing 
in speech delay actually has a dip at 25 micrograms. When 
I saw your slide 11, vaccines contributing to mercury 
distribution at three months, the scenario for 25 
micrograms, actually 75% of that group does not get a 
Hepatitis-B dose at birth, and 25% does. I guess I just 
wanted to make that comment that it appears to me as 
though more work needs to be done on the Hepatitis-B 
dose at birth scenario. 

Dr. Verstraeten: I don’t know if people managed to follow that because we 

discussed this before lunch. 

What happens in the graph for speech disorder is that you 
have sort of a dip in the third category of 25 micrograms, 
which is something we were rather puzzled about. One 
possible explanation would be that in this 25 microgram, 
the majority of those children received the DTP-HIB 
combined and received no Hepatitis-B, so they were a little 
bit at a lower risk because they didn’t received that 
Hepatitis-B in the first three months, also in the first month, 
and that would be a possible explanation. 

However, some of the analysis at three months, I have done 
them stratified on whether or not these children received 
hepatitis-B in the first month. For some of the outcomes, 
this relationship still persists. Meaning that you cannot 
explain it entirely by the hepatitis-B effect in the first 
month. Also what Phil will say, it’s not dependent on this 
hepatitis-B, so you can’t entirely blame the whole thing on 
the hepatitis-B in the first month. 

Dr. Johnston: I wanted to go back to power point 18, it’s page 9 on the 

hand out we got this morning. Why is there such a 
difference between the Group Health Coop and the 
Northern California Kaiser? Even at no injection or no 
cumulative mercury exposure, there in speech delay there 


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is almost a doubling almost all the way through. What’s 
the explanation? It is listed as 18. 


Dr. Verstraeten: 
Dr. Johnston: 
Dr. Verstraeten: 

Dr. Johnston: 

Dr. Verstraeten: 


Dr. Lewis: 

Dr. Verstraeten: 

Dr. Johnston: 

Dr. Verstraeten: 


For the rates. 

The difference in rates. 

I don’t know. I’m not sure. Why the incidence rate for 
speech delay is so much higher at Group Health as 
compared to Northern California Kaiser. 

If they are doubled all the way through the extent of the 
exposures. 

One thing that Bob just mentioned is that at Group Health 
they have their own referral center for speech and learning 
disabilities, and it seems that sort of facilitates the General 
Practitioner or the Pediatricians to more easily refer the 
children because it is within the HMO and it is probably 
taken care of. So that might be one reason why more of 
these kids are picked up. That is one hypothesis. I don’t 
know if the people at NCK want to say something. Ned? 

Ned Lewis, NCK. That’s right, and also the speech therapy 
is not covered. 

So that is one hypothesis. It appears not be covered at 
Northern California Kaiser. 

But it makes ydu a little worried about the endpoint? The 
outcome? When it is so different from location to location. 

Right. Also what we are doing more and more is the 
analysis separate for each HMO because we sort of realized 
that we can’t compare those two. 


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Dr. Oakes: 


Dr. Verstraeten: 


Dr. Oakes: 


Dr. Verstraeten: 


Dr. Rhodes: 


Dr. Rap in: 


Dr. Verstraeten: 


I am wondering if it is feasible to stratify the analyses by 
pediatrician? By diagnosing pediatrician? 

We haven’t been able to do that. One thing that Phil is also 
going to mention is stratification by health facility at least, 
and that we can do only for NCK because we know at each 
facility a diagnosis was made, and that also plays a role. 
Then of course there is still the level of the pediatrician 
which we haven’t been able to reach. 

Is that a feasibility problem, a data problem or a conceptual 
problem? 

I’m not sure, Frank, if you have an idea on that or want to 
comment on that. Any or all feasible or if you have an idea 
about that? 

I think at NCK I was able to assign a sort of usual clinic to 
most of the kids. I think going beyond that is really 
impossible at the level of data we have now. I’m not even 
sure whether in these clinics if there is the pattern of the 
same pediatrician seeing the same kid over and over or 
whether it is just who is available, and I’m not sure how 
that goes at Group Health or NCK. 

I wanted to know if the endpoint, this diagnosis of 
language disorder, autism, Tics or whatever, was it done 
just once? I mean, to enter your statistic, if the kid had that 
diagnosis once at whatever age he’s in for that diagnosis? 
How does it work? I don’t understand. 

The main bulk of the results I have shown you is just once. 
However, we have done it specifically for the ones that 
were diagnosed more than once. That is one table I have 
shown you, but only for a few outcomes have we done that. 
And also it only works for those that have quite high 


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numbers because it is only like less that half that comes 
back or are diagnosed twice. But in general it is only once. 


Dr. Davis: 


I want to actually start off my talk in a little unusual 
fashion and acknowledge the amazing amount of work 
Tom Verstraeten has done. I am not sure if people realize 
that this has been sort of a full time occupation. This is 
really a remarkable piece of investigative and analytic work 
that Tom has done, with help from others certainly. 

That said, I am going to talk today and try to address at 
least one of the concerns people have. Which is that so far 
all of the analyses done to date have been based on the 
automated codes and yes, we have used different slices of 
the automated coded, but we are still using the automated 
codes. 

So in fact we, over the last three weeks, have done a chart 
review of over 1 ,000 charts at Northern California Kaiser 
and at Group Health, specifically looking at children with 
speech delay, autism and attention deficit disorder, to try 
and answer this particular question. Which is how good 
are the automated codes? And then specifically are they all 
similarly accurate? That is, is speech delay automated code 
as good as autism? Is it as good as attention deficit 
disorder? 

Then further on, does accuracy differ between institutions? 
How can we use this information in terms of children being 
referred to specialists and speech therapy? And what kind 
of role can we assign to the history of past and present 
otitis media, and the role of other conditions in how well is 
the use of automated codes. 

Then at the very end I am going to show you some brief 
data where we have actually, or Tom, has actually redone 
the entire analyses that you have just seen using only cases 


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that were verified as being “real cases” and using different 
definitions of real. 

There is at least an hour’s presentation here and I know I 
have 15 minutes, so I am going to go through this rather 
rapidly. 

*•< 

When we look at speech delay in particular, we find that, 
believe it or not, some times it is not even mentioned in the 
chart and this is just a recurrent theme. It is not coming as 
new to those of you who have done chart review. Of the 
577 cases of speech delay, we found it mentioned in the 
chart 560 times, or 97%. Of the entire group, 91% were 
referred to a specialist, so 91% of everybody who had an 
automated diagnosis of speech delay actually was referred 
to a speech specialist, and of the original group 75% were 
confirmed as having a speech delay by a speech specialist. 
Then a smaller percentage were referred for speech 
therapy. 

There is a question we will see later on, but for this 
diagnosis at least, speech delay being mentioned in the 
chart does not vary between HMOs. 

In terms of the proportion that gets referred to the specialist 
as we saw previously, at Group Health there seems to a 
slightly increased rate of children who are referred to a 
specialist, and again even though this is a small difference 
it is probably related to the fact of the easy availability of a 
language pathology center that is specially designed to take 
care of these children. 

Of those that are confirmed by a specialist, this is the 
original number we started out with, but a higher 
proportion are confirmed by a specialist at Group Health 
than at NCK. And as Tom, Frank and I found at Group 
Health, when you were referred to a specialist it was almost 


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a done deal that you were almost in fact confirmed by this 
specialist. 

You will see that there was a much higher proportion of 
children at Group Health who are referred for speech 
therapy than at Northern California Kaiser, and these of 
course relate primarily to coverage issue. 

Now in terms of a search for pre-disposing factors, this is 
actually going to be important in what I will talk about 
tomorrow, but I will mention it today and put a little seed 
in your mind. Which is that serous and chronic otitis 
media, by history being mentioned by the pediatrician or 
the specialist, was present 38% of the time. It was slightly 
more present among Northern California Kaiser patients 
than at Group Health Cooperative. 

Serous otitis media or chronic otitis media being actually 
present at the time of the first visit was present less than 
5% of the time among these children, and only 4% of the 
children actually had a hearing loss that was tested and 
confirmed, either at the present time or in the past time. 

This again speaks to an issue I will raise tomorrow, but it 
was interesting to us how often other possible pre- 
disposing factors for speech delay were present and 
recognizable on the chart. Bilingual language in the 
household, mental retardation, attention deficit disorder, 
developmental delay or other developmental disabilities, 
overall approximately one out of four children who had 
speech delay had one of these pre-disposing factors. And 
of course simply the presence of one of these pre-disposing 
factors should not lead us to attribute the speech delay to 
the pre-disposing factor. It actually is all tied up with the 
relationship between the pre-disposing factor and the 
speech delay itself. 


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In terms of autism, there was a code and the code occurred 
120 times and autism was mentioned in the chart 92% of 
the time. It was actually coded. Of these 110 that were 
mentioned, 105 in fact were referred to a specialist. I have 
a feeling the reason that they were not all referred simply 
refers to the fact that some people were probably censored 
from the data set before they could be referred or they 
disenrolled and enrolled in a different health care plan. Of 
these 105 that were referred to a specialist, 99 were 
confirmed by a specialist and 6 had some other diagnosis. I 
imagine that would be suspicious for autism, but in fact 
turned out to be something else. 

There were really fairly limited differences between the 
two sites in terms of the predictive value of the autism 
diagnosis. When it was mentioned in the chart, around 
90% of the time it was found in the chart at both sites. 

At Group Health Cooperative, when we saw a patient who 
had autism mentioned, 92% of the time they were in fact 
referred and very similarly at Northern California Kaiser. 
Note the very small number here, so it would be one more 
or less case would actually affect this percentage point by 
eight percentage points, so I consider these equivalent. 

In terms of confirmation by a specialist, again 92% of the 
patients at Group Health and 81% of the patients at 
Northern California Kaiser had the diagnosis of autism 
confirmed by a specialist. 

Now I think we get into somewhat different findings, 
which are attention deficit disorder and attention deficit 
hyperactivity disorder. I don’t think the findings here, the 
fact that they diverge from the previous two diagnoses, is 
in fact going to surprise anybody. ADD was coded 348 
times, and in fact we only found it 249 times, 72% of the 
time, which was somewhat less than we had previously. It 


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Dr. Myers: 
Dr. Davis: 


was referred to a specialist quite a bit less, 49% of the time, 
and was confirmed by a specialist even less, 31% of the 
time. So the predictive value of these codes is only 31%. 

Our ability to fmd ADD if it was coded was similar 
between sites. But in fact, being referred to a specialist 
really diverged. Marty, you probably know about this. 
Who is the specialist at Group Health? We have somebody 
who has in essence devoted his entire life to the treatment 
of ADD and I thought he worked with you on the practice 
parameter for ADD. 

They do have a center for it. 

And that’s what I’m getting at here. They actually have a 
center for the diagnosis and treatment of ADD and ADHD 
and I must say being a pediatrician 10% of my time, it 
would be a joy to have a center where you can easily send 
children for the proper diagnosis and care and this is not 
available at Northern California Kaiser, and probably 
accounts for the difference in predictive value of this 
particular diagnosis. 

The diagnosis is confirmed more frequently at Group 
Health, probably using some standardized criteria. 

Just to wrap up this section on the confirmation of 
automated diagnosis, how good are the automated codes? I 
would say for autism, the predictive value of an automated 
code is 81% and I rate that as very good, using my 
completely subjective rating code that I came up with last 
night. It’s good for speech delay, with a predictive value of 
75%. And it is also poor to fair, that is if ADD is in fact 
coded, you only have a 3 1% chance of finding a confirmed 
diagnosis of ADD or ADHD in the medical record. 


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Does the accuracy of these codes differ between the 
institutions? I must say that I did not find any consistent 
differences, although one can make an argument that the 
accuracy may differ for ADD, ADHD and probably relates 
to center differences or the availability of specific centers 
and perhaps reimbursement practices. 

I think I am just simply going to specify that. To my take, 
the speech delay attributed to hearing loss or otitis media 
problems, by our chart review we found on 4.2% of 
children whose speech delay was directly attributed by 
some medical examiner to hearing loss or otitis media 
problems in the past. I would have to say that the medical 
record review is of tenuous value for this purpose and 
simply not worth it to go after this particular historical 
facet. 

Now you are probably ail wondering we did this medical 
record review, how are we going to use the results? Well, 
in fact we have replicated the analyses. Let me walk you 
through it because there is a lot of data packed on two or 
three slides. 

This is the relative risk for speech delay per microgram of 
exposure. So we are back to that unit or metric of 
exposure. This is all cases with the rejoinder that Dr. 
Rapin mentioned. This is now the relative risk for all cases 
of speech delay, where the cases had to be seen at least 
twice. So it is not the ones that came in, that were 
evaluated and were felt not to be speech delay. 

Per microgram of exposure by one month of age, the 
relative risk was 1.018 with the confidence interval as 
shown here. 

Now one might imagine that would just disappear once we 
actually confirmed these diagnoses from chart review, but 


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in fact it did not. You see if the diagnosis was mentioned 
in the chart, the relative risk increases ever so slightly. I’m 
not going to get into an argument of whether that is a true 
increase or not. As a matter of fact it did not disappear. 

In terms of when we cut it a little finer and insisted that all 
patients had to be referred to a specialist or had to be 
confirmed to a specialist, in fact the relative risk was down 
1 .026 with confidence intervals of slightly tighter than seen 
originally. Which is actually kind of interesting because 
the power fell somewhat. The power fell actually about by 
34% here, so the fact that the confidence intervals tightens 
up a little bit in the face of a fallen power is a little 
interesting. 

When we look at exposure by three months of age, again 
using the prior definition of all cases, relative risk of 1.013 
and if we limit it to children whose diagnosis is mentioned 
in the chart, children who are referred to a specialist or 
children who were confirmed by a specialist, the relative 
risk stays about the same, with a relative risk of 1.016 
among children who we were measuring the exposure at 
three months of age and whose diagnosis were confirmed 
by a specialist, with a confidence interval of 1.006, 1.026. 

Now this other information that we collected. Again, we 
are just comparing it to the standard here. 

If we are looking at the exposure at one month of age, the 
diagnosis of speech delay was in fact mentioned in the 
chart. We’ve excluded those children where the speech 
delay was attributed to a past history of chronic serous 
otitis or chronic otitis media, and we have excluded all 
those children who had mental retardation, bilingual 
family, attention deficit disorder and other contributing 
conditions. The relative risk in fact increases 1.025 with a 
confidence interval as shown. 


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If we limit it to children where the diagnosis was 
mentioned in the chart and we excluded any children with 
past otitis media, where the hearing loss was not attributed 
to the past otitis media. It’s just children with a history of 
past otitis median, the relative risk is similar to what was 
seen just previously. 

Now we are getting finer. If we eliminate the children 
confirmed by specialist, excluding those whose speech 
delay is attributed to past chronic otitis media and we are 
also excluding children who have other contributing 
conditions. The relative risk is now 1.031, confidence 
interval as shown. And if we are limiting it to the even 
smaller group o>f children that are confirmed by a specialist, 
and excluding any children with a past history of frequent 
otitis media, the relative risk is 1 .029. Note that this for 
exposure at one month of age. 

Now we are going to look at children whose exposure is at 
three months of age. So exposure at three months of age 
again is all cases where speech delay was seen at least 
twice. I’m sure you have all caught on, so I’m not going to 
belabor this, but you can see in fact that I think we can say 
the relative risk certainly does not disappear and doesn’t 
vary much. 

Now with autism, if we limit it to children with exposure at 
either one month or three months of age, and cases of 
autism that were seen at least twice, there is a relative risk 
that is no different than one and that is replicated whether 
we limit it to children with a diagnosis mentioned in the 
chart where the child was referred to a specialist, or the 
child was confirmed by a specialist. We see no difference 
from one. If we look at children where visits were more 
than twice and where the diagnosis was mentioned in the 
chart, referred to a specialist or confirmed by a specialist, I 


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don’t see any evidence that there is a departure from a 
relative risk of one. 

And now on to the final slide where we look at attention 
deficit disorder, attention hyperactivity disorder. Looking 
now at exposure of one month of age. If we look at all 
cases where they were seen for ADD at least twice, the 
relative risk is 1.006 with wide confidence intervals that 
include one. 

Restricting it now to cases where the diagnosis was in fact 
mentioned in the chart, relative risk is still close to one. 
Referred to a specialist, relative risk of 1 .007 and where a 
diagnosis of ADD was confirmed by a specialist, again 
1.01 with confidence intervals somewhat wide. Today at 
least, and including one. 

Where we look at exposure at three months of age, looking 
at all cases, relative risk of 1.008 and now with a 
confidence interval that skirts significance of 1.000, with 
an upper limit of 1.016. When the diagnosis is mentioned 
in the chart, it is about the same. When we limit it to 
children who are referred to a specialist, or confirmed by a 
specialist here in particular, the relative risk is 1.021 with 
confidence intervals now that exclude one. 

One might say that these are eight relative risk calculations, 
however, they are certainly not independent, so I’m not 
sure that multiple testing actually holds in this particular 
case. 


So I am going to wrap up. I’m not sure that we should 
actually have questions right now. Maybe one or two, but I 
think this would lead best right into Phil’s discussion, 
unless there is some burning questions that simply can’t 
wait. 


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Dr. Verstraeten: 

Dr. Davis: 

Dr. Cordero: 

Dr. Davis: 


Dr. Stein: 

Dr. Davis: 

Dr. Myers: 

Dr. Sinks: 


Just something you ought to mention. This condition of 
having been mentioned at least twice only applies to 
speech, not for ADD or autism. 

Thank you, I did not understand that. 

Just a clarification on the autism, did you find in the record 
review any evidence of regression or was that possible to 
get out of the records? 

There were only 13 cases of autism and I looked at a good 
number of those. I was actually looking for that out of 
curiosity. I don’t recall any cases that I ran across, and I 
don’t know if Frank or Tom, I don’t think so. We had 
Chart abstractors do the review at NCK. Did you happen 
to hear about that? It’s a very specific type of autism that it 
supposed to occur in about 20% of autism where a child is 
normal until some time of age and then has an acute 
regression. 

Did you say the 1,000 cases you reviewed were randomly 
selected charts? 

Were these a random selection? I’m trying to remember. 
Certainly everybody was speech delay, autism and 
attention deficit at Group Health. I am trying to remember 
if they were random selection at NCK. 

I think they were all cases of autism, all cases of ADD and 
all cases of speech that were mentioned twice. 

You did a very nice job of looking at these records, and I 
want to complement you on that. It strikes me that what 
you’re really showing is how well the records are reflected 
in your automated system, and not necessarily that these 
individuals are more or less true cases, because in fact 
except for the last one you showed, almost all of these 


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cases were in fact down this differential in terms of being 
referred and they are almost all the same set of kids. 


Dr. Davis: 


Dr. Myers: 


Dr. Johnson: 


Dr. Rhodes: 


Yes, I think in a previous lecture Tom talks about it this 
way and I have no better way to put it. He said something 
is apparently worrying these parents and they are bringing 
these kids in and that’s causing them to show up 
repeatedly. Now whether that is a measure of parental over 
reaction, I don’t think we can discern that, but they all 
seem to share the same attributes. So almost no matter how 
much you slice the pie, they all seem to be going through 
this data set with the same set of covariants and exposure 
metrics. 

We’re not defining a true case by a different set of 
diagnostic criteria other than the specialist has agreed with 
something else, but this is the case. 

We can have some more questions on this subject later, but 
let’s let Dr. Rhodes do his thing. 

Thank you for inviting me to speak today. First I want to 
commend Dr. Verstraeten on more work than I would ever 
do in the course of a couple of years. 

I think it is important to understand, I have been looking at 
the data set for about one month and Tom and others have 
been looking at the data set for upwards of six months or 
so. I am not going to comment on everything he has done. 
Obviously some of the things he has done are quite new 
and I have not taken a look at those. 

I think I had sort of two purposes in mind in going through 
the analyses I’ve done. One was just a very quick 
verification that there wasn’t some crucial missing 
statement in 4,000 lines of programming, and there wasn’t. 
Tom’s programming was all perfectly clear. 


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I also wanted to try to take a different look at the data 
because I think some times we make choices soon in our 
analyses. We conceptualize the problem very quickly and 
then everything else kind of depends on those initial 
choices and we don’t always go down other pathways. 

I will take a few minutes to talk about what it is I think we 
are about in this data set. What questions are answerable in 
this kind of data. Where does Thimerosal into that 
continuum and I will talk about what I saw as at least some 
possible difficulties with Tom’s early analyses, just in the 
sense that there were things that raised red flags with me 
and I know they would with other people. It doesn’t mean 
that they would affect the analyses by taking into account, 
but that they were worthy of at least taking a look at. 

I think we will see that I will approach the data analysis in 
somewhat of a different way, and I will talk about what 
some of the results are when I look at the data in somewhat 
of a different fashion. 

The Vaccine Safety Datalink study data set is an amazing 
resource that is very good at doing certain things, and not 
so good at doing other things. In terms of vaccine safety it 
is good to excellent in evaluating exposure outcome pairs 
where the outcome is acute, medically well-defined, has a 
high probability of coming to attention and has a clear 
onset occurring a short time after exposure. Especially if 
the effect of the exposure on the outcome is transitory and 
this is still possible and works even better in cases where 
the exposures are almost universal, but there is some 
sufficient variation in the age and exposure. As an 
example in which the VSD is very good at finding an 
association for example is seizures occurring after DTP or 
MMR. 


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Now if MMR had the effect of raising the relative risk of 
seizures forever, it would be much more difficult to study. 

Those pairs that are harder to evaluate is where the 
outcome is chronic or not so medically well-defined. For 
example, speech delay. Or where the onset is not well- 
defined, and in these cases if the exposure is nearly 
universal, we are really stuck with trying to compare 
groups that do or don’t have the exposure. In many cases 
them, the group that doesn’t have it will be a small, 
unrepresentative group. For example, if we are trying to 
study the effect of attention deficit disorder after MMR. 

Now you might think I am going to say it is impossible to 
study Thimerosal in this cohort, but I am not going to say 
that. But where does Thimerosal in developmental delays 
fall in this continuum? 

The outcomes here certainly do vary on their medical 
certainty. There is quite a bit of difference on autism 
versus speech delay in terms of medical certainty, and also 
the likelihood of coming to medical attention at some point. 
For example, just the orientation and the facilities available 
at the different HMOs can have a great effect in terms of 
whether certain things come to medical attention or not 
and/or are followed up in that context. 

These outcomes in most cases are chronic and the time of 
onset is not well-defined. We are also in a situation where 
the exposures are nearly universal and others have argued 
that the completely unvaccinated do form an 
unrepresentative sub-group? 

So are we in a hopeless situation? No, there is variation in 
the amount of Thimerosal by the type and manufacturer of 
vaccine. If there wasn’t, or if there weren’t changes in 


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vaccination policy over time, then we would be in a more 
or less hopeless situation. 

People have also eluded to this. Are we studying 
differences in cumulative Thimerosal exposure at some 
age? Well, that is what we are studying, but are these 
differences in cumulative exposure due to the policy of the 
HMO or the clinic we are talking about, or due to the self- 
selection of the parent. For example, lateness in getting 
vaccinated, a reluctance to accept any vaccination or 
medical care? 

Now, just as in the kitchen where the chef chooses the 
ingredients they are going to use, the kids you choose to let 
into your analysis can have a great effect on what happens 
eventually. 

In one of the areas in seeing some of Tom’s early 
presentations, I did have some concerns and I thought that 
others would have concerns. And even if it ultimately had 
no bearing on the outcomes, the fact that certain choices 
had been made might cause some problems. One of these 
was in the sense of what exclusion criteria was set in terms 
of the kids being the analysis. 

To briefly summarize, they had to be bom into the HMO. I 
have no problem with that. We are looking at early 
vaccination exposures at an early age. It is crucial that we 
feel that we have that exposure information, so I have 
absolutely no quarrel with that. 

Follow continuously for at least one year, and he didn’t 
really mention this, but that we actually only use their first 
follow up period because some substantial number of kids 
do dis-enroll and come back to the HMO. I have some 
problems with this, although not too much in some context 
and a little bit in other context. 


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Some of the others that will cause more concerns are that 
there is no using of prematurity codes, although in some 
cases they are almost synonymous with low birth rate 
codes. Probably one of the biggest is they not have one of 
the many possible perinatal conditions. A more minor one 
is that they not receive any hepatitis-B immunoglobulin 
and one that probably should be a little more controversial 
and hasn’t been is whether they get two or more polio 
vaccines by age one. 

I want to say at the beginning that all of these exclusions 
had good intent and good thought behind them. They 
weren’t just randomly chosen exclusion criteria. For 
example, the prematurity exclusion. It is easy to see that 
these kids, certainly at the extreme values, would be much 
less likely to receive HepB and other vaccines, but 
especially HepB at an early age and they may be much 
more likely to have some of these outcomes of interest. So 
especially if we are looking at the analyses at one month, if 
we leave these kids in, we are going to put high risk kids 
into the unvaccinated group, unfairly raise the baseline rate 
and unfairly or at least miss an association if one is there. 

Similarly, I think children received two or less polio 
vaccines in their first year may not be accessing the system 
as often as others or they may have a very different outlook 
on what constitutes a condition that requires medical care. 

I think I am not the only one that has been struck by the 
difference in what has been caused by some of these 
exclusions. For example, there is a whole range where 
actually there were 23 separate ICD-9 codes that were 
included in the so-called perinatal exclusion codes. When 
you look at this by HMO, there is quite a difference in 
terms of how many kids get excluded from NCK versus 
Group Health. About 19% at NCK and about 7% at Group 
Health, which is certainly a startling difference. 


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Also, if you look across the different birth facilities at 
NCK, you see a range of about 13% to 36% of the kids are 
being excluded just on the basis of these codes. This 
doesn’t include whether they are excluded by other codes, 
it is just looking at that possible exclusion criteria. 

Now some of these ICD-9 codes are likely to represent 
fairly minor occurrences. For example, 767.1 is scalp 
injury at birth. They are also very different across the 
HMOs as you would expect There are over 6,000 kids 
being excluded at NCK for this code, and only 24 at Group 
Health. 779.3 indicating some sort of feeding problem. 
About 4,000 at NCK and a little over 500 at Group Health. 

The prematurity codes are also coded differentially at the 
two HMOs. About 5% at Northern California and a little 
less than 2% at Group Health. I don’t know if you are 
familiar with this, but they actually do have a fifth digit that 
gives you some sense of what the birth weight was, and 
from these codes you can see that over a third of those that 
were excluded at Group Health actually are not low birth 
weight, but they are premature. Only 5% for those at 
Group Health, so obviously there is a very different style of 
coding for the prematurity code at those two HMOs. 

The other exclusion criteria of interest, two or more polio 
vaccines in one year and if the first enrollment is greater 
than one year, obviously this has some concerns if you start 
wanting to use events that occur at less than one year. 
Some statisticians would take offense at having exclusion 
criteria that happen after the event. It is rarely wise to 
condition on the future. It’s like counting your chickens 
before they hatch in some respects. Although certainly 
then if you only count events that happen after one year, 
there is no problem in doing that. 


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I think one problem that I have not found any solutions for, 
but there are substantial problems, at least at NCK, in terms 
of the enrollment dates. There are a lot of kids who come 
in and out of the HMO for various reasons. It is not clear. 
They may have three or four enrollment periods. They also 
actually receive a lot of care during these so-called 
disenrollment periods, so it is not clear whether their 
disenrollment is related to the fact of their parents changing 
jobs or whatever, they are still covered and it just appears 
that they are disenrolled. That is a problem I have not 
solved. One thing it does add up to, when you make all 
these exclusion criteria and you look at some of the 
outcomes, you will see that around half of the total events 
have been used in some of these categories, which is 
certainly of some concern. 

Another area where I have had substantial concerns and I 
think others also, remembering back to Tom’s slides about 
how many kids fall into the different exposure levels. You 
remember across the two HMOs combined in his cohort, 
there were about 2% to 3% of the kids were in each of the 
zero, 12.5 and 25, then a huge jump when you reached the 
other ones. So about 7% of the kids were in the zero to 
25% group and over 90% in the other groups. I certainly 
had concerns that they were an odd group in some ways, 
and other people have also raised those concerns. So just 
some further evidence that they are not like the other kids. 

n+t#' For example, when you take the three month classification 

and say what happens to these kids a little later on? If you 
look at them even seven or 14 days later, you can see that 
there has been substantial movement from the zero and the 
12.5% group. For example, after seven days at NCK, fully 
27% of the zero group has received some sort of 
vaccination in the next seven days, and 42% have received 
some vaccination in the next 14 days. Some of those are 


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***** 




receiving 62.5 micrograms of Thimerosal in that 14 day 
period. 

Now the 25% group is much more stable than the 12.5 at 
that point, and if you look at the 37.5 or 50 there is hardly 
any vaccination in those groups as you might expect. They 
have basically received what they are going to get until 
they reach the next milestone. 

To a large extent here, at least in the zero group and to a 
large extent the 12.5% group, we’re analyzing lateness 
more so. We are certainly analyzing a difference in 
Thimerosal burden by age, but if you move the line back a 
little bit to three and a half months, you would have 
substantially different exposure groups. At least in these 
lower exposure groups. 

This one is a little busy, but it is very much in line with 
some of Tom’s slides. That there may be less medical care 
utilization in the low exposure groups. 

This is the average time since the last well-child visit (ICD- 
9 V20.*), from the dis-ehrollment time back to the last 
well-child visit. How long has it been on average, and if 
you look across and this is how long you’ve been followed. 
For example, those who were followed greater than 48 
months, there were 1,500 kids. This is actually NCK and 
not Group Health because there aren’t this many kids in 
Group Health. So if you go across and look at 0-25, you 
can see that the average time since their last visit is 
somewhat longer for the lower age groups. It is not a very 
large difference, but for example, 19-24 months there is 
about a month or more difference on average since that last 
well-child visit. If you look at the proportion of those kids 
who have had a visit say within the last year or the last two 
years and you get to the older age groups, there is a 


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reasonable difference between those who are in the higher 
exposure groups than those in the lower groups. 

Another factor that was raised by the CIs at NCK was that 
there can be substantial clinic differences in California. 
Northern California is geographically much more widely 
dispersed than Group Health. Group Health is essentially a 
much smaller area than Northern California. 

And birth facilities and clinics often do have different 
policies. For example, the use of HepB vaccine in the first 
month of life, and this is for all children bom into HMO in 
1992-1998 at NCK, there was a range of 4% to 85% for 
any usage of HepB in the first month of life, with an 
overall mean for all those kids at about 43%. 

There are great differences in the exposure groups. I 
haven’t defined these yet, but we will see this in a moment. 
I through V range from 37.5 micrograms. II and III are 
different again, to 50 for 62.5 and 5 of 75, and I will define 
these in a minute. But just to show that for the largest 
clinics at NCK had very different distributions of those five 
exposure groups. 

As my epidemiologist friends in the audience will point 
out, they vary on exposure, so we don’t carry them unless 
they vary on the outcome. Well, the clinics do vary on the 
outcome, although of course at this point you don’t know if 
they vary on the outcome because they vary on the 
exposure, but at least we can verify that there might be 
some chance for confounding at this point. 

For example, taking the category of all developmental 
delays and looking it at by clinic and all children followed 
longer than four years, there was an overall percentage of 
these conditions at 4.4%. For the 32 clinics that any 
substantial number of kids, there was a range of 1.6 to 


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8.7%. This is the distribution of how many clinics had 
what percentages. So I think there was a reasonable 
enough variation there. Of course that variation could well 
be due to the exposure, but at least at this point I think we 
have enough evidence to think that clinic is worthy of 
consideration as a possible confounder. 

Again, this information is available at this point only at 
Northern California. It is not available at Group Health. 

I think at this point I was led to the idea and I sort of 
stepped back a bit. We have had the question posed of, can 
you answer the question of what is the effect of 
Thimerosal, going all the way from zero up to 25, up to 50, 
up to 75 and through 100. My various explorations through 
the data led me to basically think that some of these 
questions could be well answered and others could not be 
well answered from this data set. Those answers that I 
thought could not be well answered from this data set, were 
answers that involved questions of what happens between 
zero and something? What happens between 12.5 and 
something? But if you look at the data that is available and 
how those data occurred, some times nature conspires to 
take observational data and make it almost look like an 
experiment. Some times it doesn’t. In this case I think the 
closest we can come to regarding this as an experiment as 
opposed to totally jumbled and meaningless observational 
data is to think again in terms of what exposure groups do 
kids fall into and how do they get there? 

This is similar to some of the slides Tom showed before, 
but essentially when you look at the data there are five 
large groups the kids fall into. These totals are going to be 
between 85% and 90% of all the kids that have entered into 
Tom’s analysis. 


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There are five Thimerosal levels ranging from 37.5 to 75. 
There are two ways to get to 50 here. When you see what 
happens here, it is also very helpful to see that all five of 
these groups at NCK, but only three of them occurred at 
Group Health, and that is an important thing to keep in 
mind. At Group Health, it is very balanced that over two- 
thirds of these kids fall into one of those groups. And 
when we start combining these two things, we can get 
funny types of analyses in the sense that we have to 
understand and remember at Group Health there are a few 
kids who have 37.5, but any comparison you are seeing of 
37.5 to anything was coming from Northern California. It 
was not coming from Group Health. Similarly, analyses 
that were coming from 75, although there is an equal 
number of kids from those two HMOs, some of the event 
rates were so much higher in Group Health, that the 75 
group was being dominated by Group Health as compared 
to NCK. 

In these analyses you can get very different results when 
you throw these things together, as compared to when you 
make head to head comparisons of some of these groups. 

So at this point my thinking was that if you want to talk 
about the effect of a difference of 25, at level of 50 versus 
75 or 37.5 versus 62.5, this is a good data set to do it. 
These kids are achieving these levels, mostly based on 
policy of the HMO or clinic at the time they are getting 
vaccinated far more than they are on lateness or anything 
else. 

Some comparisons kind of jump out at you in the sense that 
we certainly would like to compare the smallest group to 
the largest group. That is 37.5 is the biggest differential we 
have. Some of the comparisons are a little more natural in 
the sense that if you think back to two of these groups that 
differ on whether they receive a DTP-HIB combination or 


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whether they received separate DTP and Hib, and actually 
at this point they could have received a DTP and HIB 
separately or a DTaP and HIB separately. And there are 
even a few kids who would have received a DT and a HIB 
separately. 

My approach was to think of terms of the analysis of the 
zero to 12.5 and the 25. I am not advocating totally 
throwing them away and never considering them in any 
analysis, but at least for now let’s think if we can establish 
if there are differences in this group of 37 to 75, then in a 
sense we really don’t need them. If we don’t see any 
difference in this group of 37 to 75, it is not that we are 
home free and we feel everything is okay, but at least we 
started from a place where we feel the data set has good 
information to offer us. And if we are going to include 
those other groups, we are going to have to think very 
carefully about how we are actually going to do it. 

In terms of how you would approach exclusion criteria in 
this study, I would have a fairly different point of view at 
one month and three months. 

At one month there are still some problematic aspects to 
this. I am not going to try to base what happens to them at 
three months in terms of a one month exclusion really. At 
least it is not very satisfying to do that. 

Here there is still some question about what are appropriate 
exclusion criteria at one month. I think most of the 
^ interest, at least in Tom’s analysis, has been at three 

months. 

There is an exclusion policy that just says the price of 
admission to the study is having achieved one of those 
exposures by three months, end of story. Don’t tell me you 
had a code of 647.2 at seven days or whatever. If the 


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choice is made to give you those vaccines by the time you 
are 93 days, you get to be in the analysis. 






The other showing the clinic was an important variable and 
led me to think it was important to think of the clinic as an 
additional stratification variable at NCK. 

The one very sobering thing that has been eluded to and is 
not obvious from the analyses that Tom presents, is that 
when you stratify very finely at time of birth, again these 
five exposure groups are very largely a matter of policy. 
Policies change very quickly over the course of few 
months. What is not apparent is the effective sample size, 
the effective number of cases that enter these analyses are 
often very different than the total number of cases that you 
see quoted. I could actually work up some slides for 
tomorrow that show how many cases really do enter some 
of these analyses. You may start with 3,000, but I think in 
some cases you may be down to 300 in terms of cases that 
actually affect the analysis. 

To try to wrap this up, if you were just looking at two 
exposure groups, for example the DTP-HIB combined 
versus the DTP and HIB separately, at NCK this policy 
choice is implemented and happens over the course of 
about two months. If you stratify finely enough and the 
policy changes are made quickly enough, you have no 
analyses because no one would be temporarily overlapped 
in order to be compared. 

The other thing that happens at NCK is that even a year or 
two years after the policy change has been made and all 
kids are supposedly receiving the combination, there is an 
odd, small group of kids that supposedly receives separate 
DTP and Hib, and an unusually high percentage of those 
kids are outcomes. 


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Then when you go back and look at their data, there is 
supposed to be information on where they received their 
vaccinations, the manufacturer, lot number, et cetera. 
Typically for some kids the facility is missing, the 
manufacturer is missing and one suspects these kids are 
those whose charts have been missed or pulled for various 
reasons and there have been data quality issues with some 
of these kids. 

For example, if 1,500 kids were receiving one vaccine 
combination in that month of birth and 20 were receiving 
some other, I have removed the 20 completely from the 
analyses. In essence, the right thing to do might be to put 
them in with the 1,500 but at least for now I have left them 
out. 

So the question is not so much the choice of the five 
exposure groups. There were two themes that came up in a 
lot of Tom’s slides. One was using the zero group as the 
comparison group and looking at how wide all the 
confidence intervals were for the other exposure groups, 
and did they or did they not overlap. 

Well, the secret is you pick a different exposure group as a 
comparison, all those confidence intervals will be different 
and some will overlap and some will not. So that is really 
sort of a false issue in some way. Also, the number of 
events was always very small in that group. 

The other thing was that his test for trend, which I 
philosophically don’t like very much because they ascribe a 
difference of zero to 25 is the same as 25 to 50, as the same 
as 50 to 75. I think in the end when you have enough 
separation and you know that your data kind of looks like 
that, I think it’s okay as a summary. But I have a 
philosophical problem with running with that analysis as 
sort of your major type of analysis. 


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He was also claiming though that if you left off the low 
groups, you could still see the trend in the groups 37 to 75, 
because that is where most of the cases are. So while I 
have left off the small groups and you will no longer see a 
comparison of 75 to zero, or 62 to zero, you still will see 
something that would tell you if there would be a trend in 
Tom’s analyses or not. Again, I am still using the bulk of 
his data, at least in the initial analyses. 

This is the only analysis I am going to present at one 
month. This is a combined analysis of NCK and Group 
Health. Using more or less Tom’s original cohort and just 
saying any or none, and using the code 315.3*, we get a 
relative risk for the anti-Thimerosal group of about 1.2, 
chi-square 12.1 and various significant P-value. 

Now adding a clinic, it doesn’t drop the relative risk very 
much, but it does increase the variability quite a bit. Now 
some clinics have almost nobody with HepB at one 
months. Some clinics have 90%. At one month you might 
say there is over-stratification, but I think it is worth 
considering here. 

Now I take all those kids that Tom has excluded based on 
prematurity exclusion codes and throw them in. At one 
month I think there is some argument that is overdoing it. 
Throwing them all back in. I think there is a clear 
argument that is going too far, but that further brings things 
'***' down. I try to bring it back up by bringing in those 

premature kids who were less than 1750 grams. It brings it 
back up a little bit. Make the polio exclusion, it brings it 
back down. So you can push, I can pull. 

But there has been substantial movement from this very 
highly significant result down to a fairly marginal result. 


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I think one could argue a long time what is the appropriate 
group to have at one month. Again, if you agree with my 
premise of what are these data good for, I think there is 
much less things to argue about at three months. 

This is just presenting data from NCK for the moment. 
Looking at the reference group here is 37.5 micrograms, so 
we are comparing our four groups to that reference group. 
We are looking at all the developmental disorders, which is 
the largest group. Due to lack of room on the slide, I have 
presented just the relative rate and the P- value for that 
relative rate. 

For example, if we start from Tom’s original cohort, these 
are all elevated compared to 37.5. Two of them are 
significant at the .05 level, but not too far beyond. One is 
very close. 

This one has a lot of cases, so it even has a very low 
relative rate. It still has a significant value. 

You could put these in different orders, but as I go down 
the list here, everything I have done in two, I will have 
done in three. So I am adding or doing various things. For 
example here, these are including all the kids that Tom 
would have excluded for various reasons. One group 
actually goes up. This group is very close to one. This 
group goes up a little bit because a lot more cases are being 
included. This group comes down a little bit. 

Now putting clinic, we see this one stays about the same. 
A lot of these come down quite a bit. The P-value are not 
becoming very impressive. 

Now leaving out those kids that have the so-called odd 
codes, I looked at the pattern of exposure group based on 
birth month and if there was some category, for example if 


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you had category four and there were say 15 kids in a 
particular birth month that had that particular exposure 
pattern, I said most of those are probably due to coding 
problems. Let’s leave those out and see what happens. 
That is something that should be followed up and verified 
that those indeed were coding problems. 

At this point three of the groups are still a little elevated, 
but none of the P-vaiues are lower than .2 at this point. 

Now looking at the speech and language delay codes, 
315.3*, instead of going through all the intermediate steps, 

I just do the original and then the final, which we would be 
using clinic. Putting back the excluded kids and tossing 
out the small number of kids that have odd codes. You see 
that they go from not quite significant, but fairly large 
relative risk to almost nothing. Are they significant? 
Nothing. High but not significant, nothing. Not much 
change there. 

Remember, Group Health did not have all exposure groups. 
They only had groups two, four and five. 

Here the reference group is 50 rather than 37.5 because that 
is what Group Health has. Here there was not much going 
on before. Maybe even a little bit more going on in this 
group afterwards, but very little change there. 

We have added more cases, so the P-value is a little bit 
lower. 

Then putting together Group Health and NCK, but just 
using Groups II, IV and V. It actually will change things if 
we include Groups I and III, but to avoid that for the 
moment let’s just focus on Groups II, IV and V. Putting 
them all together. Not much going on before. Not much 
going on after. 


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So what have I concluded from my reanalysis? You don’t 
have to agree with me, but these are my conclusions. 

That there are strong uncertainties about the fairness or the 
comparability of the low exposure groups. 

That these concerns are much less for the groups starting 
37.5. But that the evaluation, even of these groups 37.5 to 
75, is still somewhat tricky because of several issues. 

That the small amount of calendar overlap for the use of 
these different policies and for the policies that led to the 
various exposure groups can really affect our analyses in 
two ways. One that very many of the cases totally drop out 
of the analysis, and that some cases who have been maybe 
miscoded can actually have a very undue influence on the 
result. 

If we have 1,400 kids in one exposure group, 10 who are 
miscoded, and maybe their miscoding is also related to the 
fact that they are a case and it did actually occur, in some 
of these birth cohorts you would see three cases out of 10 
kids, or a similar number out of 1 ,400 kids, it is clear those 
kids are having an undue influence on the results. 

I think it is clear that at least in some respects the original 
exclusion criteria were too extreme. I don’t think they 
have affected things as much as, for example, accounting 
for clinic practices at NCK, but I think it was worth taking 
that step of thinking what were exclusion practices that 
wouldn’t at least have caused people to have trouble with 
the results. 

Overall there were still some slight tendencies for the 
higher exposure groups to have somewhat higher rates, but 
the P- values were in general quite unimpressive and for the 
most part were .20 or even much higher. 


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What do I perceive as being some of the limitations and/or 
extensions of what I have presented here? I think it is 
reasonable to argue that a complete rejection of these low 
exposure groups may be too severe. The 25 group may not 
be nearly as bad as the 0 and 12.5 group and one may be 
able to do something with that group. 

I think one cannot certainly take data where there is such a 
restricted range of Thimerosal and say Thimerosal is fine, 
give as much as you want. We looked at a restricted range 
of Thimerosal in just one particular way. 

I don’t see any big differences in these groups looking at 
this. That doesn’t answer all possible questions about 
Thimerosal. If you don’t have those ranges, you can’t 
answer about ranges you don’t observe in your study. 

I think I would say that I don’t feel there is any fair way to 
compare 0 with say 50 or 75 at three months, at least in the 
data as we now have it. 

I used a fairly crude measure of clinic at NCK. I think with 
a little more work one could use a better measure and 
actually track his over time. Most kids do stay at the same 
clinic, some do change. I just picked where did you go 
most often, but obviously there are changes in where you 
go and that could affect, things. But with a little more 
coding and a little more time, one could actually track that 
a little better. 

I think it is very important to check the assumption that 
these kids who have these unusual coding patterns at NCK 
are actually in fact miscoded. 

But I think what it also argues for is that in fact the data 
were too stratified by month of birth and that there should 


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'm*' 


Dr. Johnson: 


be some backing off, so these temporal overlaps don’t 
throw most of the cases out of the analysis. 

I will now welcome comments or private discussions with 
Dr. Walker and Dr. Oakes on what might be a proper way 
to accomplish that. A fair way. 

Certainly as already has been evidenced, the data from the 
chart reviews have been used to refine case definitions, at 
least in the analysis that has been done so far. They 
certainly haven’t made things go away in those analyses. 

Certainly there are also even from these two HMOs, there 
is more variability in the exposure within birth cohort in the 
latter part of the follow up. In ’97 and ’98, there was much 
more difference in terms of exposure categories, so as these 
cohorts age you have more an opportunity, at least in these 
restricted ranges. 

What would one want? One would want somehow within a 
situation where there is comparable ascertainment, you 
would like kids who got very low level, 0 to 25, whatever 
micrograms of Thimerosal, versus those that got 75 or 100, 
but to have the same number of vaccinations. That they are 
equally vaccinated, but because of policy differences or 
manufacturer differences, have big difference in 
Thimerosal usage. But also in which you have equal 
ascertainment, and that may be rough. Getting both of 
those at the same time may be very, very difficult. 

We have a fair amount of time dedicated at the end .of this 
day for debate and rehashing of the data presentations. I 
think it would be better to move on to biologic plausibility 
than take a break, and then come back and put all this in 
one pot and discuss it. If you have a short procedural 
question, Dr. Guess, that will be fine. 


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Dr. Guess: 


Dr. Oakes: 


Dr. Bernier: 


Dr. Johnson: 


Can we get copies of those overheads? It is very difficult 
to take notes. That was really excellent. 

Because there was a lot of information for those who 
haven’t seen it before. 

We will try to do that, but we failed to put on Dr. Rhodes’ 
graphs that there may be errors of fact or omission, and for 
our purposes this is a very important piece of writing to put 
on every one of those, so 1 am reluctant to release that until 
we try to get that done tonight and have these for you 
tomorrow. If you need one to look over and you can tum 
back over to us tonight and promise not to copy, we can 
maybe we can do that. But I think it would be best if you 
get them tomorrow when it has that on there. 

While I am making that point, let me just reemphasize if I 
could the importance of trying to protect the information 
that we have been talking about. As many of you know, 
we are invited here. We have asked you to keep this 
information confidential. We do have a plan for discussing 
these data at the upcoming meeting of the Advisory 
Committee on Immunization Practices on June 21 and June 
22. At that time CDC plans to make a public release of this 
information, so I think it would serve all of our interests 
best if we could continue to consider these data. The ACIP 
work group will be considering also. If we could consider 
these data in a certain protected environment. So we are 
asking people who have done a great job protecting this 
information up until now, to continue to do that until the 
time of the ACIP meeting. So to basically consider this 
embargoed information. That would help all of us to use 
the machinery that we have in place for considering these 
data and for arriving at policy recommendations. 

Dr. Roller? 


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Dr. Roller: 


’«***#' 




You are probably wondering why a veterinarian was 
invited to address this distinguished group of professionals. 
That question is not for me to answer, but I am very 
pleased to have been invited to participate in this meeting 
and to enjoy the beautiful surroundings of this facility. 

, 4 - 

Most of you do not know me and I do not know you, so I 
thought I would give you a brief background for myself. 

My background has been quite diversified. I am a D.V.M., 
Ph.D. Ph.D. primarily in pathology, but my research was 
with oncogenic viruses and immunology. I then took my 
first job with NIEHS, where I pioneered the field now 
known as immunotoxicology, then quickly moved to 
academia and had worked many years in my field of 
interest, which is pathology, toxicology, immunology and 
carcinogenesis. Evaluating the effects on numerous 
chemicals, including mercury and methylmercuiy, and 
today I am presently focusing primarily on auto-immune 
diseases. 

It is interesting that I have a publication here that came out 
in the year 2000 of the Journal of Auto-Immunity. The 
Title “Vaccination and Auto-Immunity”. Vaccinosis, a 
dangerous liaison. So this is another aspect of vaccination 
that is of concern to the medical professions. 

I was even foolish enough to venture into administration. I 
was Dean of the College of Veterinary Medicine for 10 
years. Then I have moved back into a more relaxed, 
rewarding life of a professor in the same college. 

I have served on many national committees, mainly for 
EPA, ATSDR, NCI, National Research Council, Institute 
of Medicine, National Advisory Committee to establish 
acute exposure guidelines for humans. Most all of these 
focusing on establishing standards for human exposures. 


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I am also presently involved with the Army and CDC in 
establishing human guidelines for nerve agents. As you 
know, we are trying to destroy all those stockpiles. 

I want to start with a disclaimer. When Roger called me I 
was just finishing up some reports for the Institute of 
Medicine committee regarding an update of health effects 
for Vietnam veterans and was starting to prepare for a grant 
renewal. I quickly dropped that and did a rush review of 
the toxicity of mercury. Primarily methylmercury, so you 
will have to pardon if I am not as thorough as you would 
like to see, particularly on some of the basic mechanisms. 

First side please. Most of you are familiar with the 
neurologic symptoms of methylmercury. There are many 
of them. Tremors, emotional lability, insomnia, memory 
loss, you can see neuromuscular effects, headaches. Pretty 
common of a lot of things. Polyneuropathies, several of 
them. Performance deficits have been recognized. Hearing 
and visual loss. Even hallucinations and photophobia. 

Next slide. What I want to do was show the daily 
consumption of methylmercury, and it might surprise some 
of you. For infants six to 1 1 months of age, about .5 
micrograms per day. Two year olds, 1.3. Females 25 to 30 
years, around 3. Males, 3.9. 

On a body weight basis for the intake, it is equivalent to 
about 0.05 micrograms per kilogram per day, except two 
year olds and that would be a little higher. 

For health professionals the values are higher. 8.2 for 
females, 8.6 for males. Health professionals probably eat 
more health and eat more fish. 

Canadians also consume a lot of fish, so you can see the 
values are higher. 


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The FDA estimates the average intake of total mercury to 
be somewhere between 50 and 100 micrograms per 
kilogram per day. 

Now the ATSDR establishes their minimum values on a 
study. It is a Seychelles child development study by Tom 
Clarkson and his group. It is somewhat of an ideal study. 
They have 700 mother/infant pairs tested from parturition 
through 66 months of age. Actually it’s before parturition. 

Mercury levels are about 10 to 20 times higher than in the 
U.S. due to the consumption of fish in their diet. The 
environment is quite pristine. The population is high 
literate. They are quite healthy with low alcohol and 
tobacco use. 

The developing fetus was exposed in utero, which as we go 
through some of the data today is going to be extremely 
important because we know the developing neurologic 
system is more sensitive than one that is fully developed. 

Neonates were continued to be exposed via breast feeding. 

What is interesting is the relationship of mercury in the 
blood, or in this case the hair, of the mothers versus the 
children. They are pretty close, and I would assume that 
even though this was at 66 months of age, the 6.5 ppm, that 
would probably be very similar as an infant and a newborn. 
Particularly because methylmercury can cross the placental 
barrier. 

Six Neurobehavioral Tests were conducted on children at 
66 months of age. Quoting the articles, “none of the tests 
indicated an adverse effect on methylmercury exposure” 
and in fact, “four of the six measures showed better scores 
in the highest methylmercury exposed group.” 


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Remember before I remove this, that even though this is 
the mean, this is the range. So we are looking at some of 
these individuals had quite high levels of mercury in their 
hair. 

The six Neurobehavioral Tests, and I am sure most of you 
recognize them, were the General Cognitive Index of the 
McCarthy Scales of Children’s Abilities. 

The Preschool Language Scale total score. 

Letter and Word Recognition. 

Applied Problems sub-tests of the Woodcock-Johnson 
Tests of Achievement. 

The Bender Gestalt Test and the Total T score from the 
Child Behavior Checklist. 

These are backed up. I grabbed these as I left town. That’s 
Faeroe Islands. There were other studies that have been 
considered to establish standards. One is the Faeroe 
Islands where 917 children seven years of age were tested. 
Basically their conclusions are that the neuropsychological 
testing indicated mercury related dysfunction of language, 
attention, memory and visuospatial and motor function 
remained. That means they still saw these after children 
and women with maternal hair mercury above 10 ppm were 
excluded. 

The problem with these studies is there were several 
confounding factors. There were higher PCB levels in 
these individuals and there were other factors. So it’s not 
as pristine an environment as you would find in the 
Seychelles population. 


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In another group, the Amazon River Basis, 91 adults with 
hair mercury less than 50 ppm. Although the clinical 
examinations were normal, those individuals in the highest 
exposures to mercury had some restriction visual fields and 
displayed some disorganized movements. 

Another study, Mancora, Peru, 131 infant-mother pairs. 
Maternal hair 8.3 ppm. Somewhat similar to the 
Seychelles. They found no neurodevelopmental 
abnormalities in children. 


Well how about blood? The ratio of hair to blood generally 
is recognized to be around 250. I have seen publications 
anywhere from 140 to 416, but 250 is usually accepted 

The other thing that has not been mentioned here today that 
has to be considered is the half life of mercury in the blood, 
particularly the organic mercuries. That ranges from 30 to 
90 days. The average is considered to be around 50 days, 
so one-half of the mercury will be eliminated in 50 days 
from the body. 


Usually the hair values lag blood by about four weeks. 


In the Seychelles study, the highest group had an average 
of 15.3 ppm mercury in the hair. That translates using a 
250 ratio to about .06 milligrams per liter of blood, which 
is 61 micrograms. 


Daily intake we won’t worry about. 


If you look at 6.8 ppm, the amount in the blood was .027. 


Thanks to Dr. Clarkson, he gave me some data in a 2000 
publication that came out in the Journal of Pediatrics and 
gave me more information. 


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There is an article by Stajich et al where he looked at 
children that were bom to term. Took newborns before 
vaccination and discovered they had .09 microgram per 
liter mercury in their blood, vaccinated them with hepatitis, 
so it would be 12.5 microgram. Forty-eight to 72 hours 
post-vaccination, their blood levels were 2.24. That was 
their mean. The range was not very large. So if you take 
that, recognizing that this is a background, very low level, I 
did some rough calculations. If it’s a linear arrangement, if 
12.5 in a vaccine resulted in 2.25 in the blood, 25 would 
equal about 5.5, 50 to about 1 1 micrograms per liter. 

Is it cumulative? Everything we’ve heard today is that 
we’re looking at cumulative exposures. I would assume 
they would need to really model out your doses and model 
into it a half life, so it is not necessarily cumulative, but 
actually the blood levels would depend on the time 
between vaccinations or the intervals. 

So I took and compared this data to the Seychelles. 
Recognizing that the mother’s hair was 6.8, their daily 
intake 34 micrograms, that blood equivalent would be 27 
micrograms per liter. That’s calculated. 

Recognizing also that this is a continuous exposure, not 
only as a child but in utero, so these children were exposed 
to this level of mercury in utero, as a neonate and during 
their childhood when they were breast fed. So we are 
looking at an equivalent in the children’s hair of 6.5, veiy 
similar to the mothers, which would be approximately 
about 25 micrograms per liter I their blood continuously. 

So I guess I can leave the final analysis up to each one in 
this room, as we have children with this level probably 
much, much higher because some of the children or some 
of the mother’s maternal hair and some of the children 
were as high as 25, or probably four times higher than that 


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Dr. Johnson: 


Dr. Modlin: 


Dr. Koller: 


Dr. Orenstein: 


Dr. Koller: 


Dr. Clarkson: 


without any abnormal neurological functional signs on the 
cognitive tests that were run by that group. 

Any questions or would you like to hold them until after 
break? 

Why don’t we have a few short procedural an then we will 
have a chance after the break to probe. 

Just a question about your analysis here. This is a term 
baby. I assume this is a term baby at the fiftieth percent for 
birth weight. What if you did the same analysis for either a 
pre-term baby or even more importantly a term baby that 
was at the fifth percentile for their birth weight? 

Well, the pre-term started out at .79 and ended up at 7.36. 
Much higher. But today the data we were considered about 
was term data. That’s why I did not include it. 

You mentioned in the Faeroes that at seven years there 
were some pick ups of language problems, attention, 
memory and visuospatial and motor. You also mentioned 
that there potentially were some other confounding issues. 
Can you make the same sort of calculations for exposure in 
the Faeroes, in terms of what levels might the kids have 
been exposed to? And do we know if any of those 
exposures were clinically significant? In other words, were 
these kids just picked up on testing or had there been any 
clinical attention because of speech delay or some other 
clinical symptomatology? 

I have that paper with me, but I can imagine Dr. Clarkson 
could probably answer that question. 

The Faeroes is a perspective study. There were no clinical 
effects whatsoever. They are simply based on an 


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examination of these children at seven years of age with a 
whole variety of neurobehavioral tests. 


Dr. Orenstein: 

Dr. Clarkson: 

Dr. Orenstein: 

Dr. Clarkson: 

Dr. Sinks: 


Dr. Clarkson: 

Dr. Sinks: 

Dr. Clarkson: 


Blood level correlates and all. Did they attempt to look 
at... 

Yes, the hair levels and blood levels. The correlation that 
they found in the Faeroes with the blood level and cord 
blood, versus the outcome of these tests at seven years of 
age. 

And was it in the same level though as the Seychelles? 
How high was the core blood... 

Actually slightly lower. Their average levels were 
somewhat lower than the average in the Seychelles for 
mercury. 

Just to point out, I think many of your assumptions still 
here underlie this basic premise that methylmercury and 
ethylmercury are similar in terms of the toxicology. I want 
to ask one question of Dr. Clarkson, because I have heard 
of this study, however you pronounce it. I have not read it, 
but I was wondering if you could comment if you have 
seen it; what you think of the quality of their exposure 
assessment was? I know your lab is very well qualified for 
looking at mercury and we have frequently seen problems 
in mercury analysis from a variety of places. 

Which study? 

The one that actually is referred to up here that was 
published this year from Mercer University and I think 
Emory may have had a role in it. 

I would have to look at the reprint again. 


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Dr. Johnson: 


Dr. Koller: 




Dr. Sinks: 


Dr. Koller: 


Dr. Weil: 


Would it be possible to photocopy that General Pediatrics 
article? 

Yes, he has it and I photocopied it for me. To answer your 
question, I have always considered the neurological effects 
of ethylmercury and methylmercury to be somewhat 
similar at a similar dose. 

Now ethylmercury has thought to cause maybe some of the 
other organ abnormalities. Maybe more so than 
methylmercury, but I have considered the responses, the 
toxic effects to the nervous system to be similar at a similar 
dose. 

And to answer the aluminum question from my point of 
view, I have worked with a lot of metals. The mechanisms 
between aluminum and organic mercury are completely 
different and I would not expect a synergism. 

Just one other comment. I do think it is important to weigh 
the difference between the quality of the exposure 
assessment; which was done in Dr. Clarkson’s study in the 
Seychelles and the amount of history he has in terms of 
that, with one study that has looked at a small number of 
infants here and how much reliability we can place on that 
data. 

Exactly. I think though if you would calculate back, and 
that is what I was attempting to do, calculate back from the 
Seychelles and the background on the human population, 
you won’t come too far off from this right here. For an 
infant. For older people it is going to be much higher. 

I have just a technical question. On your data from the 
daily intake of 34 micrograms per day, you assumed a 
blood level of 27 micrograms per liters. 


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Dr. Roller: 


I think that’s... 


Dr. Weil: 
Dr. Roller: 

Dr. Pless: 


Dr. Brent: 

Dr. Koller: 

Dr. Brent: 

Dr. Koller: 


That doesn’t fit with the data... 

Just a minute, I probably should say milligrams per day. 
Let me see. That’s milligram per day intake. 

Just a quick comment on the study. They were looking at 
premature infants and they had 15 of them and the 
confidence intervals, the range of measurements was 
extremely wide. It is hard to know how they sampled this 
little kids, and that is why perhaps they got such an 
incredible range after a dose of vaccine. And I think the 
measurements were done within 48 hours or about 48 to 72 
hours after vaccination. They only had five term infants in 
that group. 

It was a very interesting presentation. It’s nice to have 
some data to discuss. You inferred that it was probably 
based on the half life, not cumulatively. That’s an 
extrapolation or hypothesis or do you have some 
confidence in that? 

What I am assuming is that if a child is vaccinated as an 
infant with 12.5 micrograms of mercury, by 50 days that is 
going to be half that value. So to be re-vaccinated in 60 
days with 25 micrograms, the total is not going to 37.5, 
See, 12... 

I understand. The other thing is with some biological of 
some chemicals, the more you are exposed to them some 
times enzymes change with regard to excretion and 
metabolism. Is that known for mercury at all or is it totally 
unrelated to experience with the substance? 

I’d say Tom is ready to answer that one. 


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Dr. Clarkson: 


Dr. Brent: 

Dr. Johnson: 
Dr. Koller: 


Dr. Weil: 

Dr. Koller: 


As you know, methylmercury and ethylmercury are slowly 
metabolized to inorganic mercury. The common mercury 
bond is broken. It’s achieved in two ways. The microflora 
in the intestinal tract break down methyl to inorganic and 
that is how we get rid of it. Methylmercury goes through 
an entroypathic recirculation from liver to bile, to intestine 
and back reabsorbed again and but for these obliging micro 
organisms in the GI tract, we wouldn’t really get rid of it. 
So does the microflora break it down to inorganic, which is 
not well absorbed and comes out in the feces. 

The other way it is metabolized is by phagocytic cells in 
almost every tissue in the body, probably including 
microglia in the brain. These phagocytic cells will also 
break down methylmercury. We don’t know for ethyl, but 
it’s probably the same mechanism. So to what extent these 
change would do us, it’s not known. It’s an interesting 
question, but that’s not know. 

Are we going to get a copy of that, too. It would be nice to 
have to read tonight so we could... 

Yes, there will be copies. 

Incidentally, these values are correct and that’s very 
interesting. I just went back and looked. It’s .034 in the 
Seychelles. They are taking in about .034 per day and this 
is their blood level, so there apparently is an equilibrium at 
some point from the intake and from the excretion. So 
those R values are correct. 

It’s hard to reconcile that with the ethylmercury levels you 
have above. 

Well, you have to remember, this is a continuous exposure 
all the way through. This is a one time exposure. 


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Dr. White: 


Dr. Koller: 
Dr. White: 

Dr. Koller: 
Dr. Brent: 

Dr. Johnson: 
Dr. Brent: 


Dr. Johnson: 
Dr. Brent: 

Dr. Johnson: 

Dr. Brent: 


Are there experiments, particularly with rodents, in which 
the effect at different developmental stages were studied of 
the same amount of mercury per weight? Grams of 
weight? 

Per gram weight. Not that I am aware of. 

I mean a comparable dose by weight at different 
developmental stages? 

I’m sure there might be, but I am not aware of one. 

With ethylmercury, but not with methylmercury. There are 
a lot of studies with methylmercury, but not with 
ethylmercury. 

What are they with methylmercury? 

Well, with methylmercury, the problem is you have the 
epidemic in Japan and then the problem with the 
contaminated wheat in Iraq, where you had severe 
neurological deficits, but the dose that those people 
received was massive. 

I am thinking about a careful comparative to... 

Well, the animal studies, yes, and the animal studies of the 
rat have a threshold in the low milligram per kilogram from 
what I recall. 

Is it different at a different developmental stage? 

Yes, in utero the embryo is most sensitive. Especially in 
the rat, the brain development equivalent to the human 
development is actually postnatal. A lot of people don’t 
realize that, but the first week after birth the rat brain is 
equivalent to the mid-gestation brain in the human. That is 


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Dr. Johnson: 




Dr. Walker: 




Dr. Rhodes: 


the most sensitive time. The major effect in Japan was 
reduced cerebellum and severe microcephaly and spasticity 
and severe mental retardation, but a very high dose. 

Thank you. Dr. Koller. Perhaps this is a good time to take 
a break. We have about a 15 minute break allocated, then 
we will come back for discussion. 

This time is now dedicated for open discussion. There 
were a lot of points raised in the early part of the day that I 
don’t think we reached any kind of satisfactory endpoint 
on, and I am sure there are questions for the presenters. So 
this is the time for an open exchange. 

Bob Chen and others are ready to show again any of the 
materia] that was shown this morning. Dr. Walker? 

This question is for Dr. Rhodes, whose analysis was very 
impressive and like a lot of people I find myself ticking off 
things that I was going to say as he covered material. 

I was both pleased and concerned though as I looked at the 
clinic analysis. As you pointed out, by restricting at the 
clinic level and maintaining the time matching, that the 
number of informative sets must have been vanishingly 
small. That raised the variance and you suggest I think 
reasonably that we could* loosen up the matching account 
for time in some way else. But it didn’t really explain to 
the big effect the clinic matching had on the point estimate, 
and I am wondering if given the very small number of 
informative sets, if it wasn’t a good chance that the 
difference was just statistical artifact and that we shouldn’t 
extrapolate too far from the analysis you’ve present us so 
far? 

Could you say the last part again? 


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Dr. Walker: 


Dr. Rhodes: 


Dr. Wallace: 


The concern is that there was so few informative sets in the 
clinic analysis as it’s been performed so far. We saw the 
point estimate go down and everybody was gratified by 
that, but I am a little concerned about jumping on an 
attractive result which was based on a terribly small 
number of informative sets. 

I think it would be good to quantify, and you make a valid 
point. I think it would be good to quantify both in the 
initial analyses truly how many cases are taking part in the 
analysis. Also as I made the point, I think there are some 
other sets of unfortunate influential cases that shouldn’t be 
in the analysis, so I think it’s good to quantify both those 
things. And also I think your point is valid, too, once we 
stratify in clinic to show how many additional cases have 
fallen out. 

I mean one way to get them back in is to loose the 
boundaries on stratification. Actually I have done that to 
some extent and it brings effects down even more 
dramatically than other things I’ve shown, which I was a 
little hesitant to show them because what I have been 
taught as a statistician is stratify as finely as possible and if 
you back off and your effects change, then that was an 
example of confounding. 

But I think in this case I am not quite so sure. I think the 
small number of sets may be so fragile that backing off is 
actually the right thing to do. 

I think you could represent time richly with lots of nots and 
get all the advantage of the matching. One other piece 
since I have the microphone. I heard in the discussion 
some kind of equivalence assumed between an analysis of 
cumulative dose and a implicit requirement that there be a 
physical accumulation of the metal in the body for that to 
be an appropriate analysis, and I should say that we do 


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accumulative dose analyses all the time on toxic drugs that 
are metabolized and don’t accumulate in the body. I don’t 
think there is a necessary connection, or that the lack of 
accumulation invalidates the kind of cumulative dose 
analysis that’s been done. 

Dr. Johnson: I think the point was made also this morning that a series of 

acute exposures are also picked up with that endpoint. Dr. 
Brent. 

1 have been glad to be here where there are so many 
statisticians and epidemiologists because I need to learn a 
lot in that area, but I want to tell you about our field of 
birth defects. 

One of the problems we have is there are about 60 birth 
defects and anybody who does a large epidemiological 
study looks for correlations with a particular environmental 
agent just on a statistical basis will end up with three birth 
defects that are statistically associated with that 
environmental agent, just on the basis of probability. And I 
noticed that in the table on page 9, you have that flow sheet 
that has all the correlations or relative risks that have been 
calculated. There are about 80 of them and about nine of 
them are positive. So some of them are there because you 
would expect them to be positive, just on a statistical basis. 
In fact one of them shows a negative association under 
neurological degenerative disease. The .987 with the 
relative risk both from the ’95 confidence levels below one 
shows a negative association. How do you look at this data 
and which ones do you assume are the statistical ones and 
which one is the real statistical association due to an 
association? 

I bring it up because we have had some major tragedies 
with statistical associations. The one that I can think of is 
the collaborative perinatal project. The collaborative 


Dr. Brent: 


» 


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perinatal project was a 50,000 patient study from 1957 to 
1965 where they looked at women who they registered in 
pregnancy and they looked at everything. They followed 
the children after birth to age seven and did complete 
neurological evaluations, IQs and as much as they could do 
in those years. When they got to birth defects, there was 
one association that came up. Congenital heart disease, 
and it was a very famous paper by Dr. Hynanen. I can tell 
you there were probably 3,000 lawsuits about the fact that 
progestational agents was associated with congenital heart 
disease. It took 19 years to remove that warning. The 
FDA in 1999 finally removed the warning on congenital 
heart disease after millions and millions of dollars of 
lawsuits and aggravation about a statistical association. 

I just want a perspective from the statisticians and the 
epidemiologists as to when you look at data like this, what 
concerns them? How do they look at this and how can they 
explain to me what it means to them? Because it confuses 
me when I look at the birth defect data. 

Dr. Davis: 




So I kind of view this as both hypothesis testing in some 
way, in a sense that our pre-study hypothesis was to look at 
language and speech developmental disorders. And I will 
be quite honest with you, almost everything else was to 
some extent a screening analysis. Except nephrologic 


Actually we were aware of the Faeroe Island the Seychelles 
Island data, so believe it or not you’ll just have to 
understand that when we went into this particular study, my 
thinking at the time was fairly rudimentary. It was 
Thimerosal equals mercury. The stuff I knew from the 
Seychelles and Faeroe Islands knew that the primary areas 
of focus were going to be on language and speech 
development and believe it or not, we also knew that 
autism would come into play eventually. So we had to at 
least study that. 


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damage, because we did know that mercury in some sense 
has been associated with kidney damage in the past. So 
that was kind of our take on it on really the very first phone 
call. Frank or Tom, do you want to expand on it? In 
essence there were two components. This hypothesis 
testing/screening component as well. 

Dr. Verstraeten: If I can say something about the number of analysis. This 

question has been raised before. If you do 100 analyses, 
with a statistical significance level of .05, you will find five 
significant ones. That makes sense. However, the level of 
significance of some of these findings go to .001. So even 
if you do whatever adjustment, that would still be 
significant for some of these. 

Now 1 agree that fortunately I didn’t put the level of 
significance for these. That would have been helpful and it 
would be useful also to have that adjustment, but that is one 
issue. 

The other one is that some of these findings are in a way 
consistent. If it would be purely random, then we’d pop up 
in ail different places and not in certain patterns and what I 
think you are seeing here. So I think those are arguments 
that why I am not very worried about the multiple 
comparison issue. 

Dr. Jones brought up a suggestion when we were talking in 
the coffee break. The collaborative perinatal project had 
50,000 parents. They registered them right from the 
beginning of pregnancy and then they followed them very 
closely. It was subsidized. Probably all those children had 
DTP. Was mercury in the DTP in the fifties and sixties? 
Well, that is still on computer and available to you. One of 
the things I have been taught about Epidemiology is 
repetition. In other words, if you could get another body of 


Dr. Brent: 


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Dr. Verstraeten: 


Dr. Brent: 


Dr. Verstraeten: 
Dr. Modlin: 


Dr. Verstraeten: 


patients and demonstrate the same thing, it makes it more 
convincing. 

I would be the first person to try and analyze that. I have 
been asking all over if there is another data set I could look 
at and try to replicate it in a very oriented manner without 
doing another analysis. 

Well, it’s on the eleventh floor of the Archives Building in 
Washington D.C. and certainly any government employee 
would have accessibility to that data. 

I just heard that we have those data here, so we can... 

I think it would be wonderful to analyze that data set. I 
would just remind people that the only Thimerosal 
containing vaccine that children got during that era was 
DTP and that they all got it, so that it may be that your 
opportunities for using that to analyze what we would like 
to see may be limited on that basis. 

Can I add another issue? Along the line of what you 
mentioned, what we are waiting for or what we are trying 
to do is replicate it in a different data set. There is a 
possibility, if I can mention this right now, that the people 
at Harvard Pilgrim are going to try and replicate our 
findings in their data set. One of the advantages is they 
have actually weight of all the children, so we will be able 
to do this by weight exactly. So we want to avoid doing 
multiple comparisons just for the specific outcomes that we 
are interested in. That’s one and then at the same time at 
the U.K., there is another data set of the General 
Practitioners, where we have also asked them if they can 
replicate our findings there. So we are waiting for those 
results. 


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Dr. Guess: 


Dr. Davis: 


Dr. Stein: 


I think that it is an excellent idea to replicate and those look 
like good places to replicate it. I would add one additional 
note of caution, however, with any replication and that is 
something that has been said before. That there are many 
people who haven’t been diagnosed with speech delays 
who have them. Relatively subtle levels of speech delays, 
so that these data sets would still have the problem of 
potentially incomplete cases ascertainment, where the 
incompleteness conceivably could still be linked to the 
vaccinations. So that if the replication doesn’t confirm the 
findings, I would feel a little bit better about it, but if the 
replication confirms the findings, you still have this 
problem with incomplete case ascertainment, possibly 
differential. So I think one way to get around that might be 
gradations of severity of the speech delay or things like that 
which might be harder and more completely ascertained. I 
am sure a lot of people have thoughts on that. 

Let me just carry on, we actually have a whole hour 
tomorrow to talk about sort of our future research strategy 
an talk about different studies that might be done 
elsewhere, including whether or not the potential sites have 
problems like you are talking about. The sample size or 
just whether or not they would be suitable to answer 
various of the biases that we have been worried about 
today. 

The correctness of this association of Thimerosal with 
neurobehavioral or neurodevelopmental problems it seems 
to be really depends on the quality of the diagnosis. That’s 
your endpoint, so I have a few questions. 

With the ADHD diagnosis, you looked really at the billing 
codes. You did your data analysis on the billing codes and 
yet it was only a little over 30% that had a confirmed 
diagnosis. Why didn’t you do the analysis on those that 


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Dr. Davis: 

Dr. Stein: 
Dr. Davis: 


Dr. Stein: 
Dr. Davis: 
Dr. Stein: 


had a confirmed diagnosis of ADHD, rather than those that 
were just put into the billing code? 

We did. Are you saying why don’t we redo the analysis 
limited just to those children confirmed with ADD? 

Yes. 

We do have that. Let’s just see if we can pull it up real 
quick. These are the results of the analysis. The ADD 
analysis, when it is limited to children who are seen at least 
twice. Excuse me, looking at the exposure. The exposure 
is calculated by micrograms of mercury received at one 
month of age. These are now limited to the number of 
children seen at least twice and then these are level I, II and 
III, let’s see if I can call on my memory here. Feel free to 
come in and help if anybody remembers these off the top of 
their head. These are children who were at least seen by 
the practitioner, referred to somebody else and then 
confirmed. So these are in fact the relative risks for 
children who are confirmed to be ADD children, and you 
can see that the relative risks are almost identical. 

If we look at the assessment of the analysis when we are 
looking at exposure at three months of age, now these are 
all children who were seen at least twice and then when we 
look at children where the diagnosis was confirmed by this 
specialist or referral on a referral, we see that’s now 
statistically significant with confidence intervals that 
exclude one. 

Do you have the same data for speech delay? 

Yes, we do. 

Is it a similar result? 


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Dr. Davis: 


Yes. 


Dr. Stein: 


Dr. Davis: 


Dr. Sinks: 


I think with ADHD, and this is a clinical observation, if 
you go to a specialist as I think they have both at Group 
Health and Kaiser of Northern California, that diagnosis is 
fairly standardized. Developmental behavioral pediatrician 
or a neurologist, I think we can believe in that diagnosis. 

On the other hand, speech and language delay include a 
wide variety of diagnoses. I really have never seen a child 
sent to a speech pathologist without coming back with 
some diagnosis. It may be very mild apraxcia or very mild 
articulation deficit or very mild expressive language delay, 
but I have never seen one not come back. Now you had 
some that didn’t, I know, but I have never seen that. 

I think you really need to look at what the diagnoses were 
and if they were significant, rather than just a diagnosis of a 
speech delay. That’s very fuzzy. 

That is a very good point and it just proved to be 
impossible, on both a preliminary look and on our large 
look. We can wish for standardization in medical records, 
and I’ll show you it simply doesn’t exist. Some kids got 
Woodcock and some kids got Bailey. It’s just a whole 
commish of stuff done to children and it seems to vary by 
who their referral practitioner is and the age of the child, 
and other seemingly random events. I think what you are 
asking for is wonderful, but I don’t think we are going to 
get it just by looking at medical records. 

I would just like to compliment the investigators for 
actually diving into the data that were available as much as 
they did. I think they have done a fantastic job of doing it. 
At the same time I think it is important that we realize this 
is not the same thing as taking these children and putting 
them through a standardized battery of tests to determine 


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exactly what they have. The fact that we see such 
discrepancies in terms of the proportion of these kids were 
referred, 30% or 40% for ADHD versus 90% for speech 
delay suggests to me that there is a very large difference in 
terms of clinical practice and referral patterns, as well as 
willingness to accept diagnoses from a referral physician. I 
think we can say that we can cull these things down, but 
whether or not they have a specific disorder relative to each 
other, it really does require more of a standardized clinical 
battery. I think at the same time they have done a great job 
of doing what they could with the data. 

Dr. Davis: Well, thanks. I think actually we all agree with you on that 

point. 

Dr. Johnson: Could I ask, Tom, does it help you at all, the fact that as 

you tracked it more and more toward the more precise 
diagnosis, the relationship held. The relative risk stayed 
more or less the same as you got closer and closer to the 
specialists? 

Dr. Sinks: Personally not so much in the speech and the first two 

things because there really wasn’t much difference in the 
numbers. Almost all of these kids were referred. Almost 
all of the referrals ended up being a confirmation, and it 
didn’t suggest to me that there was really much difference 
in those groups if I remember. 

The more troubling one to interpret was the ADHD. The 
thing there that is troubling to me is why is it that 60% of 
50% of these kids are not getting referred? Is it an issue of 
the degree of their condition or that the primary treating 
person is very comfortable in treating this case and it isn’t 
just simply not referring them. So I am not sure. It is 
reassuring that there is something going on in the data. If it 
just disappeared, yes, but again I think in the first two 


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examples we are really dealing with almost the same cases. 
I don’t think you are culling that many out. 

Dr. Verstraeten: Can I quickly comment on that? In the first place on 

speech, we only looked at those kids that were diagnosed at 
least twice, and we already saw that the relative risk was 
higher in that group than the general group. So already we 
started out with a group of children that were more likely to 
be truly affected than others. More ADHD, so that might 
explain why the percentage was so high for speech. 

The other issue, I think you are right about the ADHD. I 
think there are a lot of General Practitioners or 
Pediatricians who feel comfortable treating these kids 
straight away with Ritalin or whatever it is. 

Another remark I would like to make, I don’t think that this 
one completely takes away the concern about parental bias, 
because one could say the more concerned the parent, the 
more likely they will see a specialist and the more likely 
the specialist will treat the kid if the parent really insists. 

Dr. Rapin: Regarding the language disorder, you must have made on 

age when this diagnosis was made, yes? 

Dr. Verstraeten: Yes. 

Dr. Rapin: Okay, two things. Number one, perspective studies have 

shown that a large number of children with early language 
delays diagnosed say at two years or three years, by the age 
of four or five years they no longer have the problem. And 
in fact, one could say it’s disappeared and then it will 
reappear at school age as a reading disability and therefore 
it is still significant. But a study from Whitehurst and your 
University at Stony Brook has in fact shown that it is a 
deficit that is predominately an expressive deficit. It 
probably is not significant. 


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The other comment I was going to make about the referral 
of the children with language disorders. The law requires 
now that all children who are at risk for developmental 
disorders be referred for intervention, so that the fact that 
so many were referred for intervention may be because 
people are following the Federal Guidelines which says 
between zero and three, if you have suspicion of a 
developmental problem, and particularly at this age 
language disorder is the first one that comes to attention, 
you will be referred for intervention. So I think that may 
be somewhat of an artifact. 

Dr. Weil: I work in the Early Intervention Program and I wish you 

were right, but in a study that we have done in Michigan, 
we think that there is less than 40%, probably less than 
30%, of the kids who are eligible in terms of delay that are 
in fact referred for evaluation. Even then we don’t know 
how many of those are getting treated. 

The total treatment group for the under three in Michigan is 
currently 2% of the population, and that is probably up near 
the national average. 

I have a question for Phil. This has to db with his decision 
to look at stratification on clinics one by one. I am 
wondering whether you think it could be, in fact, done 
where you in essence look at the clinics by quartiles of 
some measure, or quintiles even. I guess I share the 
concern that you may have actually lost a lot of the 
informative risk sets by stratifying so finely on clinics. 

Dr. Rhodes: I think it’s not so much that the risk sets have been totally 

lost, but that they are looked at quite differently in the 
sense that a clinic where 90% of the kids are in the same 
risk group. That’s a higher risk group. The case being 
from that high risk group is certainly not an unusual event. 
Now you average that across a bunch of other clinics that 


Dr. Davis: 




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Dr. Johnson: 
Dr. Clarkson: 


Dr. Verstraeten: 


are very different exposure levels, that kid may seem 
unusual. But if stratification is the right thing to do, then 
throwing them back into a melting pot isn’t really the right 
thing to do. 

It’s not good to increase your variance, but if everybody is 
the same in the group the kid comes from and you think 
that they are very different from somebody else, then they 
really shouldn’t be there. 

Actually I will say though, by the time I have included 
other kids that were excluded from another analysis and 
done everything else that I have done, the variances are 
really no bigger. Well, they are slightly bigger than before. 
The clinical stratification does increase them quite a bit, but 
doing everything else brings them back down to almost 
where they were, so it’s not that I’ve just doubled the 
variances and that’s why there is no affect. 

Please, Dr. Clarkson. 

On another topic, you heard us toxicologists talking about 
body weights and what sort of blood levels we might 
expect in this population. Do the investigators have a 
Histogram of birth weights in this study? I haven’t seen it. 
It might give us an idea of the sort of maximum blood 
levels we might expect to see in this group. To see whether 
these levels might overlap the lower ranges from other 
epidemiological studies. 

All I can say about the birth weight, at least for the group 
of which we had the birth weight, was that the mean was 
3.5 kilos and it ranged from one to about five, but I could 
produce you a histogram by tomorrow if you would like 
that. 


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Dr. Clarkson: 

Dr. Verstraeten: 

Dr. Oakes: 


Dr. Johnson: 

Dr. Oakes: 

Dr. Rodewald: 


Well, as long as you have a rough idea of what the range is, 
it would help. 

I can show you that by tomorrow. 

Everybody I’m sure is very well aware of this, but I don’t 
think it has been explicitly mentioned. There must be a 
whole range of other potential confounding factors that we 
don’t have data on. Can’t measure predisposing to these 
various conditions. I guess it would be helpful at some 
point to kind of prioritize a list of what these might be and 
whether there is any hope of getting any kind of handle on 
it. 

Do you want to start the list of things that would worry you 
most? 

Well, I guess we have some data. What have we 
considered so far? We have some data on socioeconomic 
status. We don’t have any kind of data on smoking, 
although it was mentioned. Alcohol. 

In terms of what Phil Rhodes was talking about in terms of 
whether the low exposure group should be analyzed or not, 
that’s a potential area to look for confounders. One of the 
characteristics that Dr. Modlin had mentioned earlier and 
the thing that’s been troubling me the most, and that is that 
the lower exposure group are by definition late starters. 
There is a lot of health service research talking about the 
characteristics of babies who are later starters for 
vaccination and delayed vaccination. Many of these are 
socioeconomic factors and poverty access to care, which 
would not be a problem in this data set. But then there are 
subtle ones. This month in AJPH in basically the same 
group of children, there is a study shown that the late 
starters have less continuity of care. They see the same 
doctor fewer times after time, and that may have some 


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Dr. Stehr-Green: 


Dr. Rhodes: 


relation to being diagnosed with something subtle. So I 
think I would go to the Health Services Research and look 
for distinctions between things associated with late start 
that also may be associated with receiving one of these 
diagnoses. 

Let me preface this by saying this may make no sense 
because of my ignorance of the etiology and pathogenesis 
of development delays and so forth, but is there any merit 
in doing some sort of time series analysis to see if we can 
demonstrate a standard period between the point of 
exposure to the vaccine and the onset of whatever the 
outcome is? Is there any sense in doing that? The only 
temporal association you have demonstrated is that the 
diagnosis occurred after they first were vaccinated. Okay, 
that’s the basic bottom line you have to demonstrate, but is 
there any merit in trying to establish if there was a unique 
or specific period of time post-exposure that development 
delays were first noticed? Does that make sense? 

I think Tom and I both have done some work on that in the 
sense that is there a different relative risk at age two as 
compared to age four, or one exposure group versus the 
others? I have not seen any big differences along those 
lines. | have not looked extensively or not across all the 
outcomes, but in the few I have looked at I have not seen 
anything that relates to that along those lines. I have also 
looked in different calendar periods. One thing that didn’t 
really come out in the earlier discussion was that the 
diagnosis rates for these conditions, if you look at kids who 
are the same age at different calendar times, they have gone 
way up in the last five or six years in both Group Health 
and NCK. The diagnosis is much more common now than 
it was four or five years ago, for speech delays as well as 
ADHD. 


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Dr. Johnson: 


Dr. Rhodes: 


Dr. Johnson: 


Dr. Rhodes: 


Dr. DeStefano: 


The comment was made this morning, or the question was 
raised are you just shifting the diagnosis to an earlier age 
and that if you looked later in time, would that even out? 

I think the amount of risk that is being ascribed to 
Thimerosal exposure is so swamped by the calendar time 
factors. In other words, if there was an effective from 
Thimerosal and you stopped giving any Thimerosal, would 
you see a decrease in these problems? No, you would 
probably still see an increase because the temporal trends 
are so strong. Unless they have platformed out, they would 
swamp any effect of the size that has been contemplated 
here so far. 

But what you just said sounds like a conclusion. That the 
temporal trend is what is being measured. 

No, no. I am saying that if one tried to do an ecologic 
study and said let’s look at what the rates have been over 
the last few years. Now let’s start not using any 
Thimerosal. Shouldn’t they go down? No, they wouldn’t 
necessarily go down because there has been a very strong 
upward trend in these two HMOs over the last three or four 
years in using, for example, the speech code, and that 
temporal trend may just keep going up and its slope is so 
much greater than the contemplated effects of the 
Thimerosal that if there were effects and you took them 
away, the trends may swamp that out totally. 

I would like to make a comment on that. I am not sure if 
this was one the slides you had, but at one time I know you 
did some logistic regression looking at kids of certain ages. 
I think even up to six years of age. There was still a follow 
up, and then just looked at the proportion that had these 
outcomes. In that case, time of onset doesn’t matter that 
much. You are just looking to see if they got it by that age. 
By the age of six, which for many of these conditions 


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Dr. Verstraeten: 


Dr. Oakes: 

Dr. Verstraeten: 


including speech delay should be noticed by then. Is that 
one of the slides you had, the end results? 

One of the concerns I had was the fit of the proportion of 
the hazard model. I am sort of surprised none of the 
statisticians have brought that up. I had a hard time trying 
to see if the fit was proper or not because the classical 
matters with all these strata was a bit hard, as I had more 
than 100 strata and I didn’t really feel like doing it for 
every different strata. 

This is by calendar year. This is still the proportional 
hazard model by calendar year, just to see if by a year of 
birth of the children to see if it was different for certain 
groups of children or not. So not all these estimates are 
identical. In general they tend to be similar. Note that in 
the last years the numbers become very small, however, in 
the first years the differences are not very large. 

But if I can have the next slide, here instead of a 
proportional hazard model, we did a logistic regression 
model. I didn’t use person time here and it’s a bit tough to 
define exactly the control group. However, if I do it for all 
ages and not looking at different years, and this is for 
speech, the outcome is almost identical to the proportional 
hazard model, which suggests to me that it is not a question 
of bringing the diagnosis forward, but it is really the overall 
number that drives this estimate. And if I do it by years of 
the children, there is also hardly any difference, except 
above four years and then it sort of goes down. But until 
four years the estimates are not very different 

Those are cumulative, right? 

They are not cumulative. 


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Dr. Oakes: 

Dr. Verstraeten: 
Dr. Stehr-Green: 
Dr. Verstraeten: 


Dr. Stein: 

Dr. Verstraeten: 
Dr. Stehr-Green: 

Dr. Verstraeten: 
Dr. Davis: 


Dr. Johnson: 


So where they say one to two years, that is between one 
and two years? 

Absolutely. 

But this is the age of onset, right? 

Age of onset or age of disappearing out of the group. The 
problem here is what is the control group I am going to 
use? So as a control group I used children that disenrolled, 
that reached the stop date before one year or between one 
and two years, et cetera. 

Does this mean that speech delays were diagnosed under a 
year of age? 

Actually they were, yes. For some children they were. 

That would be a very important point with regard to the 
accuracy of the study. Do you know how many? 

No, I haven’t looked at that. I have no idea. 

You and I both know it should be about zero, but it’s about 
eight and it’s children who aren’t making sounds yet. 
Frequently these children haven’t made sounds and they 
had an older sibling with a profound speech defect. I 
actually saw a couple of these and the parents wanted to 
make sure that they were sort of lining up the services that 
were available. At least at Group Health. That wasn’t 
actually a rare scenario. 

If the number was small, hearing loss would be another 
possible explanation. 


Dr. Rapin: 


I would like to make a comment. We have been focusing 
on all these acquired causes including mercury and 


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Dr. Johnson: 

Dr. Rapin: 
Dr. Johnson: 
Dr. Rapin: 
Dr. Johnson: 


Dr. Rapin: 


prematurity, and you had a list of confounding variables 
that should be considered in future studies. What we know 
today about all of the developmental disorders is that 
environmental factors are in fact rather unimportant in the 
case of these deficits and the major cause is genetic. So I 
think in future studies it would be extremely important that 
some genetic data be obtained. Questions such as is there 
anybody in the family who has reading difficulty? Because 
we know that the outcome of severe language disorder in 
the preschool child, the vast majority of the children will 
learn to speak, but they will reappear in the second bump of 
the condition as poor reading and spelling in childhood and 
adult life. I find it a little difficult knowing this and putting 
in autism. The major cause is not environmental, it is 
genetic and that we are focusing just on these 
environmental events or adventitious events when we 
haven’t considered, and you told us that you don’t have 
data for example on siblings, your study does not lend itself 
to considering the major variable. 

Well, I think the assumption is that those genetic 
predispositions would be randomly distributed. 

But you don’t know that. 

No, that’s an interlining assumption. 

1 understand that, but you don’t know that. 

Just on principle, Dr. Rapin, it seems to me that the more 
we learn about genetics or the more we learn about let’s 
say autism, the more we shift toward focusing on genetic 
causes, but would you rule out the possibility, and let’s 
move away from autism, that some of these are genetic 
predisposition and then the second hit? 

Not at all. I think it is in fact an attractive hypothesis. 


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Dr. Johnson: 


Right, thank you. Yes, Bill. 


Dr. Phillips: 


Dr. Verstraeten: 


Dr. Oakes: 


Dr. Modlin: 


I wanted to return to what I think was a thread that was just 
beginning. We talked about temporal trends and now we 
have talked about non-environmental causes. What is the 
population attributable risk we are talking about? Even if 
we assume that all children completed the complete series 
of immunizations and they all include all Thimerosal 
containing vaccinations, what is the burden of illness that 
we are talking about for these areas of interest? Speech 
delay and ADHD, that could possibly be attributable, if we 
believe these figures, to this exposure? What is the public 
health impact of the findings? 

I haven’t come around to calculating the attributable risk. I 
think it would be a bit tricky because we have different 
exposure categories, but I think it would be possible for 
each category to assign an attributable risk. As you are 
aware, however, a large majority of children are 
vaccinated, so it will probably be quite high, if we believe 
the signal. 

On that calculation though, whether you choose zero as the 
baseline or the lowest or the largest exposure grouping 
would be a critical choice. 

Two things. One I was just about to make the comment 
that I hadn’t heard anybody use the term attributable risk 
for other reasons. 

Secondly, as a non-statistician, let me ask a naive question. 
That is here we would have to assume if you use the term 
attributable risk, in part because the relative risk albeit may 
be significantly different are still extremely low, the risk 
ratios are low, that the true attributable risk is going to be 
low. It’s just that when you apply even a very low 
attributable risk to a very large population, a large 


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Dr. Oakes: 

Dr. Phillips: 

Dr. Johnson: 

Dr. Verstraeten: 


Dr. Johnson: 
Dr. Snider: 


denominator, then the actual absolute numbers become 
very important. Is that right? 

If you express it as a proportion of cases it is. If you 
express it as an absolute rate it would be, but as a 
proportion of cases which is fairly rare anyway... 

How about expressing it as the number of people per 
100,000 population? My question is what is the public 
health impact of these findings? 

Could be large. 

Maybe to make a general remark on this, I have been a bit 
reluctant to get into such types of calculations. I think in 
the first place the whole face of this study was just to 
produce a signal, and what you are asking now is to 
extrapolate this to a public health level, which I have 
always been reluctant to do. I think in the first place that is 
giving credit it is not due, and in the second place, it is 
giving more accuracy to this data than what they really 
have. 

Dr. Snider? 

I have two questions, but I will pose them one at a time. I 
am wondering from our mercury experts what they thought 
about Phil’s presentation with regard to using I guess the 
37.5 micrograms as a base, and then comparing the 50 and 
62.5, 75 to that, and whether those differences in dose in 
the vaccines, whether based on knowledge of the effects of 
mercury, they would have expected to see the kinds of data 
we saw or something less or something greater? Given 
how much that dose would contribute to blood levels and 
tissue levels? 


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Dr. Clarkson: 


Dr. Johnson: 

Dr. Brent: 


Dr. Snider: 


We went through this calculation last August, and that’s 
why I am asking about the body weights. It would help a 
lot to keep us out mischief tonight if we had a reasonable 
idea. We have already heard what the range of body 
weights might be from one to five kilograms. It would be 
awfully nice to know what they would be from two months 
to four months to six months to get some feel for where 
these blood levels might lie. 

Again, in doing such types of calculations, we have to 
assume it bears like methylmercury, which probably is not 
quite correct. But we could come up with some blood 
levels that would sort of relate to these that have gone on 
before. 

It might be that in the very low birth weight group at the 
end of six months, we might start to approach some of the 
lower limits, where you would expect to see a small risk. 
But I don’t know beyond that. 

I’m sorry, Bob, what was that comment? 

I said the problem is the greatest risk for 
neurodevelopmental problems in a premature is the fact 
that they are premature, not the fact that they have gotten a 
vaccine with mercury in it. I mean it is very hard to sort 
that out. You know the high risk of neurodevelopmental 
problems in a 23, 24 or 27 week old premature. 

I think I got the answer to my question. Basically that 
without knowing in greater detail the weights and being 
able to calculate the dose based on body weight, it is hard 
to know whether this is what you would have predicted or 
not. 

The other question is for Phil or anyone who has had the 
opportunity to look at his analyses. That is if we turned the 


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Dr. Rhodes: 




problem around and said well, we have these data that 
suggest there is an association between exposure to 
vaccines and presumably the mercury component and these 
health outcomes, why is it that it goes away with the 
analyses that Phil has done? I am just trying to get a clear 
idea of the answer that question, because I think that is also 
important to be thinking about it from that perspective as 
well. 

It is just hard to absorb all of this data at one time. I have 
had the luxury of seeing some of it more than once and I 
am sure some of the people who have never seen it are 
feeling swamped. The one thing I recall is the issue of the 
coding and some of the clusters of some strange coding, 
but I am wondering if Phil or some others would do a 
critique of that and say what might have made the effect for 
the moment we will assume is real, disappear with those 
adjustments he did? Would you be willing to criticize 
yourself, Phil, or do you want the other people to? 

I think some of what I did is not directly comparable to 
what Tom did in the sense that I haven’t computed slopes if 
you will in that restricted range. For example, it is possible 
that even though say with five groups, I started with 
comparison group in four groups. A couple are significant 
and that goes away. It is still conceivable that you would 
see a mildly significant trend in those five groups, even 
though none of the four comparison ones are anywhere 
near significant. So to be strictly comparable with what 
Tom has done, I would have to go back and compute a 
trend statistic if you will for those three or five groups or 
whatever I have in my analysis. 

I think the criticism has been made that maybe stratifying is 
too severe at NCK. I think the clinic is a variable that can’t 
be completely ignored. It is going to require some looking 
at. 


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Dr. Snider: 


Dr. Rhodes: 


Dr. Snider: 


Dr. Rhodes: 


I think the biggest criticism of both of our models at this 
point is that they are over stratified on month of birth. That 
we really aren’t analyzing as much of the data as we 
originally thought we were analyzing, and that some 
thought this is a great idea to control and an inadvertent in 
fact in a number of different ways, whose effects are still 
not totally understood. 

I guess to push a little more. It seems to me in addressing 
the question posed earlier about what epidemiologists are 
concerned about, and we have probably beaten on it 
enough, but I think we are worried about some kind of 
confounding in which there is an association between 
receiving vaccines or at least receiving vaccines on time, 
and the attention parents and health care providers might 
pay to their children’s speech patterns and other behaviors, 
and therefore there is a greater likelihood of case 
ascertainment in that group that is well vaccinated. And 
there may be just as much disease in those that are not as 
well vaccinated, but there is not as good case 
ascertainment. That’s the major epidemiologic concern I 
guess that I have. 

But there is also this big junk of patients that fall out of the 
analysis, by excluding those kids who had some kind of 
diagnoses at birth. If I understand your analysis, Phil, 
including them seems to wash out the effect considerably. 

I think it has different effects. It goes indifferent 
directions for different outcomes at different times. It 
doesn’t uniformly tend to bring things down. 

And I am confused as to how that happens. What could be 
going on that creates that kind... 

Again, I don’t think there is any uniform effect that brings 
everything down. I think there was some that were actually 


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greatly strengthened by including those additional cases, 
apart from the fact that there were just more cases. There 
were some that went up and some that went down. 

One feature that this does interact with potentially was 
clinic at NCK. There was one slide that showed the 
proportion of kids. The sequence of events here is that 
there are hospitals, birthing facilities at NCK, and they 
typically feed to one, two or mostly three clinics. So when 
you look at the birth facilities there was a huge variation in 
the proportion of kids that were excluded by these perinatal 
exclusions. That also varied by time for some reason. So 
putting these kids back in was a different thing for different 
clinics. For some clinics you were putting back in about 
13% of their kids, or maybe about 10% because some 
would still be excluded, but for others it was putting back a 
quarter of their kids. So there was certainly a lot that could 
be going on in that sense. 

Dr. Snider: And I believe you pointed out that some of them were what 

we might term clinically at least, relatively trivial 
diagnoses and others were quite substantive diagnoses. 
They are very heterogeneous in terms of their clinical 
impact, and there was a broad range of the frequency in 
which various clinics ascertained these abnormalities. 
Therefore, as you point out, the percent that are withdrawn 
for those reasons varies considerably from clinic to clinic. 

Dr. Rhodes: Most of those perinatal codes are from the hospital 

discharge record from the birth. Now that is not always 
true. Some of these came from later ages and it probably 
wasn’t even appropriate to their use, because they came 
when the kid was like a year old or something. But on the 
other hand, one hospital leads to two different clinics and 
there may be some relationship there between what the 
hospital will pick up and what the clinics will pick up. 


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Dr. Chen: 


Dr. Brent: 

Dr. Verstraeten: 
Dr. Rapin: 

Dr. Verstraeten: 

Dr. Modlin: 


To come back to the first point Dixie raised in terms of 
how do we get around this problem? A kind of health care 
seeking behavior bias potentially from parents. One of the 
reason that led me personally to not be so quick to dismiss 
the findings was that on his own, Tom independently 
picked three different outcomes that he did not think could 
be associated with mercury and three out of three had a 
different pattern across the different exposure levels as 
compared to the ones that again on a priority basis, we 
picked as biologically plausible to be due to mercury 
exposure. 

Now Harry Guess kind of challenged us earlier to say that 
maybe those three aren’t good outcomes. In which case 
then perhaps it would be useful for us to come up with 
what other additional ones do we want to test? If maybe 
five out of five or ten out of ten, all of those have a 
different pattern, then maybe that would be a way in which, 
based on these results before we do all these necessary 
studies, but those are going to take a much longer time, 
may be a quicker way to get at our answer. 

Which one of the three that would not be associated with 
mercury? 

One was conjunctivitis, diarrhea and injury. 

Flat feet. 

There were two additional I added later that I thought 
would be more susceptible to parental worry, and that was 
flat feet and the code called worried well. Diagnosis not 
confirmed by physician, that is what it means. 

I know the hour is getting late, but one of the things we 
have not discussed is the date regarding premature infants 
in any detail that I found interesting. I understand that the 


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Dr. Verstraeten: 


Dr. Modlin: 

Dr. Verstraeten: 


premature babies were analyzed. That these were babies 
that were just premature, but had no other diagnostic code. 
Is that the case? They were all the prematures, so they 
could have been associated with all sorts of 
confounding...well maybe that by itself is probably reason 
not to try to delve any further. But it was interesting that if 
you exclude the kids who got no vaccines for reasons that I 
think we all agree were likely to have been the most 
severely affected kids and therefore not immunized, it 
would be of some interest to note the timing of the vaccines 
for the other kids. Did the others get theirs on time or were 
they delayed? Or you would guess that they might be 
delayed. And whether or not you can’t pull out of those 
groups the infants who had a diagnosis of just prematurity, 
but no other diagnosis such as developmental delay. Well, 
I am getting myself into a circular argument here. 

It might be more comparable, what I have here is all 
premature, no matter whether they had anything else or not. 
Except they had to have two polio vaccines still. That was 
still there. So basically for like the entire category, the 
trend is even downwards. I am not sure what would 
happen if I took out the zero category. I am not sure if that 
becomes really flat or if there is still some kind of trend. 
However, the part that worried me was this. If I do the 
DTP-HIB combination, I can come up with relative risks of 
more than four, which is really very high. But the issue 
here is that as you can see at these confidence intervals, the 
numbers are quite small. We are still talking about 300 
children. That is for the six months. The other one would 
be a smaller number. 

It’s unspecified delay, not a speech delay. 

For the speech delay that doesn’t happen. That’s different. 
With speech delay it doesn’t happen, so once again that is 
inconsistent here. I don’t know about the prematures. I 


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Dr. Modlin: 


Dr. Verstraeten: 


think it is a very hard group to look at. If I take out the 
ones below 1 500 grams, these risks come closer to one, but 
it is still not one. So that explains part of the effect, but not 
entirely. 

The bottom line to me is you can look at this data and turn 
it around and look at this, and add this stratum, I can come 
up with risks very high. I can come up with very low risks, 
depending on how you turn everything around. You can 
make it go away for some and then it comes back for 
others. 

To me the bottom is well, there is some things that just will 
never go away. If you make it go away here, it will pop up 
again there. So the bottom line is okay, our signal will 
simply not just go away. 

I guess Pm thinking out loud here, but it might be that for 
some future study, that actually focusing on premature 
babies may make some sense, because we have all said 
from a biological risk standpoint, we would expect them to 
be at highest risk from exposure to Thimerosal if it was so, 
and it might be that designed to stay focused on those 
infants that don’t otherwise have an obvious explanation 
for a cognitive problem might be a reasonable thing to do. 

Maybe one thing to do would be to take the approach that 
Phil has taken. Saying that if they have reached 37.5 by 
three months, they probably didn’t have a lot of problems, 
otherwise they wouldn’t have received those vaccines. 
Although I am not sure because I can see some veiy 
premature children also getting vaccinated. I don’t know, 
Frank, if you want to comment on other studies they have 
tried to do with prematures. It is usually not very 
straightforward. I was thinking of the neonatal mortality 
study. That was pretty impossible. 


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Dr. Johnson: 
Dr. Sinks: 


Dr. Verstraeten: 


Dr. Oakes: 


Dr. Verstraeten: 


Dr. Johnson: 

Dr. Snider: 


Is it related, Tom? 

It’s related to this. I caught the issue. The biggest concern 
we had with analysis was when they showed us the chart. 
If I am not mistaken that first no-dose category. 
Everything else was way below it and isn’t that driving 
these numbers here? 

Yes, that’s what I was mentioning. I think so, but I would 
have to look at those numbers, but anyway there is no 
upward trend. Of that I am sure. 

Well, I don’t know that you can say that in looking at what 
those other figures look like. You can’t say what it would 
look like without taking the zero group away. It might go 
up. 

I know it doesn’t. I know it didn’t. I’m not sure if it’s 
above or below one. It’s no different from one, but I’m not 
sure. 

Dixie, did you ask your second question? 

Yes, .1 think I asked my second question, but I think I 
would just like to respond on the other diagnoses. 

I think this was a reasonable effort and I, too, like Tom 
want to take the opportunity to congratulate the people who 
have been analyzing this data set. It’s tremendously 
difficult working with administrative data sets and trying to 
make some sense of them. But I think it still might be 
worthwhile trying to give some consideration to some other 
diagnoses that might not have the same hard endpoints that 
conjunctivitis and some of the others did. I think flat feet 
and the worried well are reasonable things to look at, but 
maybe giving some additional thought to diagnoses that 
parents would consider as potentially serious problems, but 


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would not have the hard objective endpoints would make 
me feel a little bit better or worse, depending on how they 
came out. 

I still remember when I first came into the TB research 
branch and had the opportunity with George Comstock to 
look a TB rates among people who had participated in 
BCG vaccine trials, but had refused vaccination with either 
vaccine or placebo. And to look at the TB rates, which 
should have been the same as the placebo recipients got, 
but in two large trials were about 50% different. And no 
one has ever been able to explain that. I think people who 
do clinical trials are aware of those kind of quirky things. I 
have realized that people who exhibit certain behavioral 
characteristics, whether it is refusing to participate or 
maybe seeking care more than other people, can have 
different outcomes and there can be disassociations, even 
though I guess we don’t understand in this case the 
psychological mechanisms of the psychobiological 
mechanisms that are operating. 

So it is not that I by any means want to dismiss this signal. 
As someone was talking about what is the attributable 
risks, there are tremendous policy implications for this. 
Not only as the issue was brought up with compensation, 
and we haven’t heard from John Clements, but for global 
immunization efforts and so forth. But I think we have to 
be very, very careful that we got it right when we decide to 
make a policy call on this. 

Dr. Johnson: Thank you for that reminder. Yes, Michael? 

Dr. Gerber: Coming back to the methylmercurialization factor as a 

possible confounder, it seems to me that with the 
opportunity in this Harvard Pilgrim study, I don’t know 
how much you want to get into this, but if there is an 
opportunity in that study to use as a reference group those 


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Dr. Verstraeten: 

Dr. Chen: 


Dr. Johnson: 


Dr. Verstraeten: 


Dr. Rhodes: 


Dr. DeStefano: 


who had no Thimerosal exposure, not because their parents 
didn’t commit for immunizations or brought them in late, 
but those who were not exposed because they received 
Thimerosal free vaccines. That would be a good way to try 
to deal with that. Is there that opportunity? 

No, they pretty much used the same vaccines unfortunately. 

Five years after the VSD, we will be able to answer that 
question. 

Could I ask. Dr. Rhodes said he was not too excited about 
trend analysis as it was used and I think Tom, you had 
commented a little on trend analysis, but how would you 
respond to that? Or Bob, either one. 

Maybe I would have to ask Phil to clarify because I am not 
sure what his critique was on the trend analysis. 

I think my basic problem with it is that the assumption that 
12.5 or whatever value you picked, 0 to 25, is the same as 
25 to 50, 50 to 75 has the same effect. Unless you allow 
those separate groups to have their estimates first and you 
see they kind of fit a pattern that kind of adds up on a 
certain scale, to me going directly to that kind of modeling 
you can certainly obscure a lot of points. I have certainly 
seen cases where you have lots of ups and down, but you 
think you have a significant trend. 

Can I comment on how this unfolds? Basically as 
unfolded, the first presentation were just by category, and I 
think it around the second or third group that Tom 
presented when they started asking or started eyeballing. 
Saying this looks like a trend. Have you done trend tests 
for this? So then Tom started putting in test for trend and I 
guess with these big tables, it ended up being a convenient 
way to summarize data on those many tables. But it did 


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start out just looking at them categorical and then basically 
the audience kept requesting trend tests. 


Dr. Rhodes: 


Dr. DeStefano: 


Dr. Guess: 


Dr. Oakes: 


I think it is important though to realize that the category 
models where you are comparing to the zero category are 
very different than the slope you go. 

Especially since they are presented on the same overhead, I 
think people have gotten a little confused. They see the 
arrow bars. The arrow bars are for the model that 
compares each of the exposure levels to zero, but then there 
is a trend statistic on the bottom, so I think people have 
gotten a little confused. 

Then I am going to come clean on that as well. As Tom 
said, at first he tried to use the biggest group as the 
reference group, and then there was a lot of arrow bars that 
didn’t overlap one, and I thought it might be better if we 
have more standard arrows that overlap one if it was going 
to get disseminated. I thought the zero group looked like a 
more logical release. When people want to see a zero 
group, I have become more convinced in the last 
intervening months that there is something pretty weird 
about this zero group or that probably Tom was correct to 
begin with, but still that’s how it unfolded. 

I did want to support Phil’s point on the issue of trend test 
and concern about them, especially the linear trend test. I 
believe there was an article in the American Journal of 
Epidemiology a few years back expressing a concern about 
it. So there are a bunch of different ways I think the 
statisticians could provide advice on how to do that, but 
there is a legitimate concern and some counter examples 
that show one can get confusing results. 

I would just like to respond that you could always kind of 
produce examples against any technique. I am not familiar 


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Dr. Johnson: 


Dr. Oakes: 


Dr. Sinks: 


with this specific article, but I think certainly the test for 
trend is a reasonable way to look at these and screen them 
as a preliminary thing, saying is there anything there or 
not? It is also a separate issue from where you include the 
zero group in there. 

The other is you can do a test for departures from trend as 
well as the test for linear trend. I doubt that there would be 
enough power here to really detect any departures from the 
patterns that you do see. 

But I think the other way of doing it and looking at each 
group separately and putting arrow bars on each group 
separately, you do dilute the strength of the relationship if 
you do that, so it’s a trade off with power to detect a 
relationship. 

For the non-statisticians, so when the confidence intervals 
consistently overlap one, but the trend is statistically 
significant? 

I know these are not continuous exposure, they are actually 
discreet, but if you imagine it was continuous and you split 
them up into finer and finer groups with smaller and 
smaller numbers of people in each group, then the 
confidence intervals for each group would become wider 
and wider, and by splitting them up into enough groups you 
would get them all to overlap one, even if there was a 
strong relationship. 

The way I have always used the test for trend is that it is 
not just simply a test for trend, but it is a test of the slope of 
the predictive curve. And that what you are really hoping 
is that you can use that test for trend when that curve 
actually is fairly good at predicting the point estimates for 
the categorical comparison. If it doesn’t predict it well, it 
usually suggests you shouldn’t be using the test for trend. 


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Dr. Oakes: 


Dr. Davis: 




Dr. Braun: 




Dr. Rhodes: 


That is kind of my rough way of looking at it. There is a 
statistical way of doing that and I think Phil mentioned that 
in his analysis. 

It depends on the correlation between what you really 
ought to be using and what you are using. That is what 
governs it. 

In defense of Tom, I think also one thing. First I agree 
with David in that one alternative is simply just to go back 
to comparing each category to the reference which does 
dilute out any signal, and you can then structure the 
categories to increase that dilution. But also I think one 
common sense approach would be to look at the observed 
trend. Here the observed trends haven’t in fact been linear. 
We are not taking curve a linear or biorhythmic trends and 
doing simply linear trend tests on them. So I actually think 
there is considerable evidence here to support the use of the 
trend test from what Tom has done. 

The zero exposure group, it sounds like Phil and Tom 
really chose different analyses there and they have an 
important impact I think on the results. I think there was 
some- evidence presented with those kind of controlled 
diagnoses, conjunctivitis and gastroenteritis, showing a 
little step there, and that group was different even on those 
curves, as well as health care utilization and vaccine 
coverage at one year. I am concerned about that group as a 
comparison group. I was wondering, when you have the 
date analyzed two different ways, you could always present 
them two different ways, but somehow I think one way 
may be preferable, and I have concerns about using that. I 
would like to hear if Phil or Tom have anything to add 
about that. 

I think my perspective was again, to look at the data and 
saw what questions do these data best answer? Not try to 


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Dr. Brent: 


Dr. Verstraeten: 
Dr. Brent: 

Dr. Verstraeten: 


start from the point of view that you have to answer this 
question regardless of whether the data is appropriate for it 
or not. And also taking a conservative point of view, that if 
you could see differences at 25 microgram levels, then you 
should be very concerned so that you wouldn’t have to 
argue about these other groups. 

So again, the fact that I don’t feel like I am finding 
anything veiy strong in the data doesn’t lead me to 
conclusively say that nothing is going on, but that beyond a 
certain level there is not a lot going on. In other words, 
that these 25 microgram differences, there is not a lot going 
on. And that whatever is apparently going on at the start, 
most of that is explainable through some other 
mechanisms, such as some of the exclusion criteria, clinic 
practices and that. 

So obviously like my approach I did it, but it wasn’t 
designed to give a definitive answer when it was negative. 
When it was positive, then I didn’t think we needed to 
argue about some of the other aspects. 

I would like to tackle this question for my education. With 
the result that you have a slope over the period of time in 
the six months with regard to the results, what explanations 
would you have for that finding? In other words, all the 
ones that you could think of, of why you got those results? 
I have some explanations, but this is not my area. I would 
like to hear yours first. 

You mean for the increased... 

For the slope of the increased risks with time. 

What time? 


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Dr. Brent: 


Dr. Verstraeten: 

Dr. Brent: 

Dr. Verstraeten: 

Dr. Brent: 

Dr. Verstraeten: 
Dr. Brent: 

Dr. Verstraeten: 


Dr. Brent: 


Well, over the six month period. I mean many of your 
curves showed the rise in the relative risk, is that not 
correct? Maybe time. I mean over a period of time, you 
give me the explanation of why over a period of time you 
got this increased risk. 

I’m sorry. I’m not sure I’m understanding why you say it is 
increased risk over a period of time. Do you mean the risk 
increased? 

Wasn’t it true that if you looked at the population that had 
25 micrograms you had a certain risk and when you got to 
75 micrograms you had a higher risk. 

Yes, absolutely, but these are all at the same time. 
Measured at the same age at least. 

I understand that, but they are different exposures. 

Yes. 

What is your explanation? What explanations would you 
give for that? 

Personally I have three hypotheses. My first hypothesis is 
it is parental bias. The children that are more likely to be 
vaccinated are more likely to be picked up and diagnosed. 

Second hypothesis, I don’t know. There is a bias that I 
have not yet recognized, and nobody has yet told me about 
it. 

Third hypothesis. It’s true, it’s Thimerosal. Those are my 
hypotheses. 

If it is true, which or what mechanisms would you explain 
the finding with? 


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Dr. Verstraeten: 


You are asking for biological plausibility? 


Dr. Brent: Well, yes. 

Dr. Verstraeten: When I saw this, and I went back through the literature, I 

was actually stunned by what I saw because I thought it is 
plausible. 

First of all there is the Faeroe study, which I think people 
have dismissed too easily, and there is a new article in the 
same Journal that was presented here, the Journal of 
Pediatrics, where they have looked at PCB. They have 
looked at other contaminants in seafood and they have 
adjusted for that, and still mercury comes out. That is one 
point. 

Another point is that in many of the studies with animals, it 
turned out there is quite a different result depending on the 
dose of mercury. Depending on the route of exposure and 
depending on the age at which the animals were exposed. 
Now I don’t know how much you can extrapolate that from 
animals to humans, but that tells me that mercury at one 
month of age is not the same as mercury at three months, at 
12 months, prenatal mercury, later mercury. There is a 
whole range of plausible outcomes from mercury. 

On top of that, I think we cannot so easily compare the U.S. 
population to Faeroe or Seychelles populations. We have 
different mean levels of exposure. We are comparing high 
to high in the Seychelles, high to high in the Faeroe and 
low to low in the U.S., so I am not sure how easily you can 
transpose one finding to another one. 

So basically to me that leaves all the options open, and that 
means I cannot exclude such a possible effect. 


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Dr. Brent: 


I think that is very helpful. I would add a couple of things 
in there and that is that there are three reasons why you 
might have the findings that you reported. One is, and we 
don’t have the data, that with the multiple exposures you 
get an increasing level, and we don’t know whether that is 
true or not. Some of our colleagues here don’t think that is 
true, but until we demonstrate it one way or the other, we 
don’t know that. 

The other thing is that each time you have an exposure 
there is a certain amount of irreversible damage, and that as 
you exposure the damage adds up. Not because of dose, 
but because of they are irreversible. 

And the third thing is that maybe the most sensitive period 
is later, like in the fifth or sixth month. In other words, the 
sensitivity period is not the same over the first six months. 

Those would be explanations that you could only 
demonstrate with research, and probably not human. One 
of the things that could be done here, since we don’t have a 
lot of human populations and that is going to take a long 
time, is to model an animal experiment. 

I was involved in the allegation that came from the ABCC 
that one rad of radiation resulted in a doubling of the 
incidence of mental retardation, which didn’t make any 
sense. We went back to the laboratory and did an animal 
exposure using nine neurodevelopmental behaviors and 
showed that at one rad, you have no pathological effects. 
The central nervous system was effective and the neural 
behavior was normal. 

I think the government could put together a project like 
that, just to see what the threshold is for neurobehavioral 
effects. You can’t use the rat to predict things in the 
human, but it could give us some information that would be 


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Dr. Johnson: 


Dr. Davis: 


Dr. Brent: 
Dr. Snider: 


a little helpful. Because the big problem is all the things 
you say about mercury are true, except the fact is it is 
important on the dose and we don’t know what the 
threshold is on mercury. If we are below the threshold for 
any effect, then all the things you say with regard to the 
toxicity of mercury are just not valid, but we don’t know 
the threshold dose. 

Bob, when you focus only a threshold, you make the 
assumption, isn’t that kind of a puristic constant. You 
make the assumption. When it reaches a detectable point 
across a population so that when you are dealing with 
human beings you have a lot of different genetic make ups 
and presumably you get the end large enough and those are 
blanked down, but if you look at individual cells you add 
these things and they affect the cells. Each individual cell. 
So you are focusing a lot on what you can measure as your 
endpoint and your determination that yes, this is a threshold 
effect. There is no gradient effect, and that worries me. In 
general it worries me. In any kind of assumption that this 
is only a threshold. 

But I think just to take what you were saying a little bit 
further, one could posit that there is a normal distribution 
of background mercury in the human population, and by 
vaccinating everybody at one single time you have raised 
that and in essence moved that entire normal distribution 
some segment to the right, and you may in fact get some 
very small, but detectable portion of that population in the 
affected range. 

Well, I don’t know. 

Just to build on what Bob said, based on earlier 
conversations about this population. One might support a 
hypothesis further by saying that if the people who are 
more likely to be on time for their vaccines are the higher 


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Dr. Brent: 


Dr. Johnson: 


socioeconomic group and they are like the health care 
workers that Dr. Koller showed us and so forth, it may be 
that their baseline levels of mercury are higher. So what 
you are doing is seeing an effect in a population that has 
higher baseline levels of mercury, and you don’t see it 
necessarily in those that are lower because first of all they 
are not getting the extra mercury anyway. So you have 
exacerbated the problem even further than what you have 
just described. 

The implications from this discussion is that the threshold 
is very near what we are talking about here. The fact is that 
we don’t know that. You might find that it is tenfold or 
even a hundred fold higher with regard to some of the 
things we are discussing. You have to do the study, 
whether it is in the human or in the animal. I mean all 
these hypotheses are valid hypotheses to test, but I can tell 
you in our field we don’t have a single agent that produces 
birth defects of the central nervous system or any other 
organ that doesn’t have a threshold. If you want to make 
birth defects with Thalidomide, you give 50 milligrams. 
You can give every mother in the world one milligram and 
nothing will happen, and that is true of every known 
teratogen. That is a typical toxic curve. That is because it 
is a multi-cellular phenomenon. It is a toxic phenomenon. 
It is not a stochastic phenomenon. We need to data to 
answer the questions that you are raising. 

I would ask our mercury experts with regard to the fact that 
I don’t think there is a spectrum of genetic susceptibility to 
mercury like there is to Dilantin and many other drugs with 
a bimodal curve. I think that is a very narrow spectrum. I 
would like to hear from our two experts. 

We are going to have to close and I will let Dr. Sinks have 
the last comment, but Bob, let me just try to explain a little 
further my concerns. If you look at fetal alcohol syndrome 


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that started out as a very striking syndrome. Facial and so 
forth, the better we have gotten at analysis and broadened 
our analyses, we decreased the fetal alcohol effects and 
further to possibly ADHD and so forth. So declaring that 
you have a threshold effect, it continues to worry me. You 
and I can discuss it later. 

Dr. Brent: Fetal alcohol effect. This is 50 milligrams per day. When 

it gets a little better, you don’t have any effects and that’s 
only a glass of wine a day. 

I want to see what the endpoints are. Tom. 

I was going to say something similar. I think it is fine 
when you are comparing apples to apples and you are 
saying the effect of this. This birth defect, phocomelia for 
Thalidomide, but when you are looking at something and 
you are changing your effect and you are looking for more 
subtle effects, and lead is a classic example where we are 
looking at more and more subtle neurologic outcomes, we 
start dropping down what that threshold might be. Because 
we have changed the way we are measuring the outcome. 
And as long as we have faith that the outcome we are 
measuring is real, then that threshold rs changing on the 
basis of what the outcome is we are measuring. So there 
are two things going along at the same time. One is the 
outcome, and the other is the threshold. You are kind of 
keeping that threshold as a constant based on the outcome. 

I think they have been saying that there is a threshold, and I 
would like to know what it is. I’d like to probe and find 
out what it is. 

Dr. Johnson: The smarter we get, the lower the threshold. 

Dr. Bernier is going to allow us to end and go out finally 
into the fresh air. 



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Dr. Bernier: 


DAY TWO 
Dr. Johnson: 

Dr. Braun: 


Quickly if I may, I want to talk about the homework 
assignment for the consultants, and I would like to invite 
the other members of the meeting to feel free to fill it out. 
We would like to collect your opinions, although the 
people we are obviously looking to are the 1 1 consultants. 
I am sure if you have seen the list of participants, you know 
who you are. 

I just want to read these questions in case there are any 
semantic issues, because we did focus carefully on this and 
we don’t want to have any semantic problems when the 
questions are answered, and then oh, that’s not what I 
meant. So let’s try to make sure that we understand clearly 
the questions. 

There are three questions altogether. As I mentioned this 
morning, I would like to suggest that you take your notes 
this evening and make notes on here as preliminary 
answers. Use that tomorrow morning to make your 
comments because we will go around the room person by 
person. There are 1 1 consultants whose opinions will be 
solicited, and then after you hear those opinions, you may 
want to make some revisions on the final sheet you turn in. 


Are there any questions that anyone wants to pose the 
presenters from yesterday? 

I have a question that did not get answered, but I don’t see 
Dr. Verstraeten here. It had to do with the presentation of 
data and Dr. Rhodes was concerned particularly about 
using that first zero group as the reference group, and all 
the relative risks that were presented on the graphs were 
based on a comparison to that group. I think that might be 
a pertinent issue to the extent that the data are presented 
here, but outside, and will affect the risk estimates. I 


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thought that might be a useful thing to consider, but I don’t 
see him here, so I don’t know. 


Dr. Chen: 

Dr. Clarkson: 
Dr. Davis: 


Dr. Braun: 


Well, he should be on his way, so why don’t we go on to 
other questions. 

You mean we are lost? People can’t find us? 

The only thing I do know is that I think on page four of his 
hand out that he had Thimerosal and then showed analyses. 
It has sort of a cryptic title, but I think when you kick out 
the zero exposure group, the relative risk to language, 
speech and unspecified delays seems to remain relatively 
unchanged. It’s kind of hard since we had so many 
analyses we were talking about yesterday. 

The thing is, if you present the data as trends, but if you 
present the data with the arrow bars and the real risk, then I 
don’t think, so it depend on how you want to present the 
data. And then if you do present then with each stratum, 
each category having its on relative risk, then it would 
affect the risk estimates. But not if you present the data as 
just a trend with one number characterizing the trends. 

I guess I shared the concerns that Phil raised. I thought 
they were valid and convincing. He left more leeway with 
talking about the next two or three exposure groups and 
said there may be some value in those. But the zero group 
seemed to be different and many of the analyses, even the 
ones where it shouldn’t have differed, so in my opinion if 
you are going to do those categories versus one reference 
group and then every category you look it in comparison to 
that group, in my opinion those are not useful to present 

Certainly there is a high risk that they are biased, so I just 
wouldn’t recommend those. I would be interested in other 
people’s opinions. So rather than recommend a specific 


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Dr. Caserta: 


Dr. Davis: 


Dr. Clarkson: 


Dr. Braun: 


Dr. Chen: 


way to do it, I would you could either do a relative risk of 
the strata versus a difference reference group. Or like Bob 
was saying present a trend number which from what you 
are saying, that wouldn’t change if you want to do it that 
way. I don’t have the same problem with doing it that way. 
I hope that is clear. 

I have a question for Bob. When you did the chart review. 
Bob, did you look at the zero group to see if there was any 
obvious difference with that group as opposed to the rest of 
the cohort, or was that not done? Can you describe the zero 
group in any other way other than saying that there is a 
zero group and that’s all I know about them, and that they 
had two polios? 

No, we did the chart review completely separate from 
exposure. We literally had no idea what the exposure was 
on purpose and I provided the chart review. Your point is 
well taken. 

Mr. Chairman, when I look at the paper here, the graphs 
don’t always say zero. The reference. They say less than 
37.5, then say less than 25, so are they all referred to zero 
or not? 

No, but even in those less than 35, they are part of the less 
than 35 group. I mean they could be excluded from that. 

I think the bottom line is that while the zero group is 
different, and I think all of us would agree with that, the 
issue is that it is impossible, unethical to leave kids 
unimmunized, so you will never, ever resolve that issue. 
So then we have to refer back from that. 

If we can never, ever leave kids unimmunized through 
these age groups in order to study them ideally as we 
would like, then of the kids who did become or are left 


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Dr. Caserta: 


Dr. Braun: 


Dr. Chen: 


Dr. Braun: 


Dr. Stehr-Green: 


unimmunized for whatever reason, be it that their parents 
are socially responsible or be it that they have some other 
pre-existing condition medically, we just have to work with 
that. 

I think if we throw them out, or maybe I think the thing to 
do is that I would chart review, I guess it would make sense 
on the chart reviews to focus on those cases a bit more to 
understand. If these kids are otherwise normal and they 
really are just not being immunized because of social 
circumstances probably, we need to make some judgment 
as to are they otherwise at risk for the outcomes that we are 
looking at. 

But Bob, a study could be done. You could use the 
acellular pertussis that doesn’t have Thimerosal in 
ComVax, and have children be immunized, but not have 
any Thimerosal. 

Sure, we will have the answer in five years. The question 
is what can we do now with the data we have. 

One of the things that Vito said was how were they 
different? I think there was a graph of the health care visits 
in the first year and they had fewer health care visits than 
the other. 

But if that is purely because they come from parents who 
otherwise are busier or whatever reason, but the kid 
otherwise is normal, would you want to throw them out? 
Are there criteria we could develop when we go to the chart 
review that would permit us to retain them? 

Well, I think the issue is whether or not they have the same 
opportunity. If they were to develop the interests, if they 
were given the same opportunity to inspect them, I think 
the answer is no. And I’m not sure any amount of chart 


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Dr. Caserta: 


Dr. Stehr-Green: 


Dr. Hadler: 


review is going to resolve that issue. They are 
fundamentally different. They have differential potential 
for ascertainment, and I don’t think the chart review 
findings are going to resolve that. 

But you don’t know that. You don’t know that there is 
differential potential. They may have gone to the doctor 
less because they weren’t as abnormal. You just don’t 
know. 

I don’t know the reason, but the evidence was presented. 
There were several evidences that were presented that 
suggested that there was lesser opportunity for them to 
have been affected. They sought health care less frequently 
than the higher exposed groups, and maybe it was because 
they were healthier and they weren’t affected by 
Thimerosal or whatever. But the fact of the matter still 
remains is that they did not have the same opportunity, if 
they developed these outcomes, to be affected. 

I basically agree with the issue of how you handle the less 
than 37 group in this analysis you presented. I think it has 
to be thought through carefully. As you can see, a lot of 
the analyses, when there are fewer outcomes they have 
already lumped them together. In others they have kept 
them apart, and yet the numbers of outcomes in much, 
much smaller than it is once you get up to 37. The real 
issue, is the zero group very different, which it appears to 
be in some analyses. The 12.5 and 25 are less clear, but do 
you really have the power to discriminate between these 
three groups or is it better to always keep them lumped? It 
sort of gives you a false sense of well, we can say there is a 
linear trend beginning at zero and going up to 67. And yet 
you really just don’t have the power, even if your biggest, 
for these lowest exposures and I think very careful thought 
with the statistician has to be given as to whether you keep 


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presenting it with those three split apart or whether you 
group them together. 


I think someone also needs to look again at these groups as 
carefully as you can to just know as best you can how 
much they are different in terms of health seeking behavior. 
Part of it is probably spelled out in some of these tables, 
but get the best understanding you can to make some 
decision. Are you going to try to split them apart and give 
a full sense that there is a difference between them, or 
whether there is a power to differentiate or to see a 
difference between them, or perhaps just lump them and 
say we cannot say below 37.5 that there is any difference 
among those group. So for the purpose of this analysis, we 
are going to put them together. 

Dr. Rodewald: I think he may have just made the same point, but it’s not 

just the zero group, but the first two are clearly late starters 
because of the first dose of HepB. And actually the first 
two and three quarters of the groups are really late starters, 
so it is the three groups. We have been just saying the zero 
group, but it is more than the zero group. 

Mr. Schwartz: The thing is we have all looked at the fancy 

epidemiological analyses. To me one of the most 
important pieces of data presented was the crude incidence 
rates. The outcomes which was on page 9 of the original 
material that Tom presented. It shows if you look at the 
incidence of speech delay and ADD, it shows that these 
outcomes in the zero group and the 12.5 group actually are 
diagnosed more frequently than in some of the groups that 
have higher exposure. 

I think the other thing that really stands out is that if you 
look particularly at Group Health, there doesn’t seem to be 
much of a trend toward those increased outcomes with 
increased exposure. 


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Dr. Davis: 


Mr. Schwartz: 


Dr. Davis: 


I talked with Tom during one of the breaks yesterday and 
what he mentioned was if you do the trend graphs for 
Group Health alone. In other words, if you separate Group 
Health and Northern Kaiser and do those trend graphs, that 
with Group Health you see an increase from the reference 
group. From the first category to the next and then straight 
lines. So you don’t see a trend with the Group Health data 
separated from the Northern Kaiser data. And that really 
the graphs that were presented are driven by Kaiser, which 
has a much larger patient population. So I think one of the 
points that is worth making is that the information we are 
basing our conclusions on are really more related to a 
single managed care organization rather I think that the 
combination of the two. And if that is not correct, maybe 
Bob could indicate that, but I think that is correct. 

I am uncomfortable having to speak in Tom’s absence, 
because he knows the data certainly better than I do. But I 
do know one problem is simply that they are crude. So I 
agree with what you are saying, in pointing out that they 
are crude. And as it pertains to the combined graphs that 
we saw yesterday, you are right. Whenever you combine a 
gorilla and a small mole, it is going to look mostly like the 
gorilla and that is what we are seeing. Northern Kaiser has 
always been bigger than Group Health and there are many 
other issues attached to that. 

But nevertheless, when we combined the data it is almost... 

Then the Group Health data are essentially clad across the 
different exposure categories? 

Well, they have a different appearance that varies by 
disease and here is the man himself, but I don’t think it is 
proper to think there is no trend at all. It has a step wise 
trend and then a flat. 


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Mr. Schwartz: 


Dr. Bernier: 


Dr. Weil: 


Dr. Bernier: 


Dr. Cordero: 


But that first step is the same thing we saw, for example, 
the first step going in the opposite direction with the 
prematures and then it was flat. 

So what that initial study was, but I would hesitate to make 
any analysis of the prematures frankly because I think there 
is so many of those confounded by variations in the 
prematures. 

I don’t think there are sufficient data, not just in this study, 
but anywhere to make the assumption that ascertainment 
depends on the number of visits. It is sort of a reflex 
concept, but in fact having done a lot of work on trying to 
ascertain developmental delay with kids under three, we 
can’t find that the number of visits determines the rate of 
ascertainment. And not in these data, but in other data that 
we ware working with, so I think people jump to that idea 
because it is intuitive, but the fact there are no data to 
support that concept that I know of. 

Jose? Dr. Johnson had to step out for a moment fora 
personal call, so I will step in for him until he gets back. 

I’d like to follow up on Mr. Weil’s comments, but if we 
look at page 5 of the additional analysis hand outs, one of 
the things that I was impressed with is that the group on the 
bar charts, and basically the groups that have 0, 12.5 and 25 
micrograms all seem to have completion rates of 60% as 
compared to the groups that have 37.5 or greater. In the 
National Immunization Survey, when you look at the risks 
on their immunization, there is some 4-P risk factors. One 
is maternal age less than 19, some lower socioeconomic 
status. Meaning a family below poverty line. Number 
three is households with five people or more, and the 
maternal indication was in high school. That is linked to 
maternal age, too. All of those factors also tend to be 


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related to the fact that the parents are going to likely to be 
paying less attention to especially subtle abnormalities. 


Dr. Orenstein: 


Dr. Bernier: 


Dr. Oakes: 


Often because the children are going to have a visit doesn’t 
mean they are getting immunized, nor are they getting 
diagnosed. Especially things as subtle as some of these 
developmental disabilities that may not get picked up on a 
single visit. 

It seems to me that when you have such small percentages 
of the population getting zero, 12.5 and 25, I have a 
fundamental discomfort of trying to say that group is a very 
strong referent group to the rest. 

It seems to me the strongest data begin at 37.5 micrograms. 
As Jose pointed out, that is the group that was finished on 
time, even though they were perhaps starting late. I think 
that if the trends are there, 37.5 is your reference group. 
Those I think are perhaps more concerned than if you have 
to start at zero. We all realize that zero is problematic. We 
saw it conjunctivitis and in others. It seems to me from the 
scientific perspective, 37.5 as the referent group makes 
sense. 

Can I ask if some of the statisticians or epidemiologists if 
they want to comment on that, and then move on to ask the 
individual consultants their opinions, but David Oakes, do 
you want to comment on this issue? 

I do find it a little confusing that the groups switch around. 
The‘ reference groups switch around with the different 
diagnoses, and the reason is the end is different and there 
weren’t enough people, but it does make it a little hard to 
compare across, so I would advise trying to make it 
consistent. But certainly I think we are agreed that the zero 
group is not a good group. 


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Dr. Bernier: 
Dr. Kurz: 


Dr. Verstraeten: 


Dr. Rodewald: 


Again, I do want to emphasize that if you are doing the test 
for trend, essentially that does not use a reference group, so 
it is one argument for analyzing the data that way rather 
than computing relative risk. 

Anyone else? Paul? Dr. Kurz? 

I have trouble, too, with the referent group because by 
using this zero exposure, because there is a lot of difference 
between zero and 25 and the other exposure group, and 
when we use this zero exposure as a confidence variable, 
they are very much an influence. They also influence the 
P- value. I don’t see a curve with a fitted variation nine 
with an exposure to see what was really the fitted line by 
using the zero exposure, because it may be an interest if 
you use all the diagnostic criteria to test this relationship 
and... 

No, I haven’t. One thing I have done was to take out the 
zero group and that does not affect the estimate. The side 
effect group is so small, it really is a very low influence. If 
you start taking out the lower groups, I know for speech I 
could take out up to 25, even I think 37.5 and the trend 
would still be there, so at least for that it doesn’t affect. I 
have not tried it for all the other ones. So I am not sure 
what the effect would be for the other ones, but I would to 
reemphasize what data that has. Once you have the trend 
test, the influence of those category groups is quite small 
because they are quite small in sample size and they are not 
a reference group anymore. There is no such thing as a 
reference group at that moment. 

In my mind, are we talking about taking out the bottom 
three groups? The below 75? Is it as if you could put your 
thumb over those points and then take a look at the rest of 
the line and say that that’s what it is, or just this really do a 
reanalysis of the different referent groups and then you 


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Dr. Oakes: 


Dr. Braun: 




Dr. Ellenberg: 


may have something that is no longer the same line? 
Because I think, at least in my mind, maybe not others 
minds, but it’s just like if we put our thumb over those 
lines, problems just go away, but I’m not really statistical 
enough to know if that is true. 

There is a difference between taking them out and 
combining them. I’m not quite sure which we are talking 
about. Are we talking about putting all these three groups 
together? 

I think you still have to be careful, even with the trend, 
because the trend line is saying for every increase in 
milligram of mercury, you are increasing the risk X, and if 
the data is really based at 37 to 75, then if you talk about 
the zero to 37 group you are kind of extrapolating. Okay? 
Because if the line is really coming from that range of data, 
then people are going to turn around and say there is 75 
milligrams, they are really going to take into account the 
beginning of the line. So isn’t that kind of an 
extrapolation? And even Alex Walker was saying 
yesterday if I heard him right, that those data are not 
influencing the line. The lower than 37 because of the 
small numbers. So in a sense there is some extrapolation 
that is going on. So in a way it is more satisfying to use the 
trend, but you are still not totally obviating the issue. 

I think we all face an interesting point that you don’t get 
into the people who actually got their full vaccination 
series until you get to 37.5. I guess I would worry a little 
bit that we started here and left the others who might be 
trading one bias for another. Because then we have a group 
of people who got their full vaccination series by the end, 
so why were some of them late starters? And might that 
relate to their medical status? I don’t know why, whether it 
would be a random thing that some started late, so I don’t 


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know what possible bias there would be with that approach 
either. 

Dr. Weil: I think there are two things we need to think about. One is, 

is there a threshold? Bob Brent raised the question 
yesterday. Are we are talking about a threshold effect or 
are we talking about from zero, so the first microgram of 
mercury has an impact. It seems since we are not talking 
about malignancy that we might very well have some kind 
of threshold phenomenon, so those values that are down 
below that threshold may in fact be of very little 
consequence. We don’t know that and we will never find 
that out from these data. 

The second point is there is something else we won’t ever 
find out from these data, I don’t think, and that is whether 
37.5 milligrams at one month is different than 37.5 
milligrams at two months or three months, and that may be 
because of brain development. A critical issue and we 
can’t answer that either from these data, no matter how 
they get manipulated or how many times we review. So 
some of the really gutsy questions from a person who is 
very concerned about neurodevelopment cannot be 
answered out of this. I don’t think we have anything that 
says this establishes this. All we can say is we are anxious, 
and we need to get data the way we ordinarily do. We need 
to go to animal neurotox studies, developmental neurotox. 
We need to look at some other data that can be obtained to 
see if we get a comparable kind of impact, but let’s not try 
and refine and refine and refine these data. These are what 
they are. They show something and you cannot, by 
twiddling them and manipulating them, get much more out 
than Tom, Bob and others have already done. They’ve 
done an amazing amount with relatively little data, and I 
think I am impressed at how much they have got and I 
don’t think we are going to get anymore out of it. 


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Dr. Johnson: 


Dr. Stehr-Green: 


Thank you. Bill. I think that is a good transition comment 
so that we could go on and move into statements from the 
consultants. We are going to go question one and we will 
then have question two. We will have a presentation 
before we go back and deal with ideas about research. 
Maybe I should read the question so you don’t have to do 
it. Do you think the observations made to date in the 
Vaccine Safety Datalink Project about a potential 
relationship between vaccines which contain Thimerosal 
and some specific neurologic developmental disorders, 
speech delay, attention deficit, ADHD and developmental 
delays constitute a definite signal? That is are a sufficient 
concern to warrant further investigation? Paul? 

First I wanted to reiterate what others have said. 1 want to 
congratulate the folks who did the initial analyses for a 
tremendous amount of work, a lot of dedication and very 
interesting results. 

In my judgment, these preliminary results are not 
compelling, but the implications are so profound that the 
lead should be examined further. 

My outstanding concerns and reasons for that statement 
really go to the validity and the accuracy of these results 
that revolve primarily around the issue of ascertainment 
bias or confounding, which I think is potentially a fatal 
flaw which was not dispelled by some of the clever 
analyses. 

Some other concerns I have deal with the uncertainties, as 
we talk about the low dose groups, and I think Dr. Rhodes 
demonstrated those concerns very nicely. In effect that is 
closely related to the first issue of ascertainment bias. 

Another concern I have is the inconsistent and in effect 
mostly unknown case definitions that again, even though 


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Dr. Brent: 


Dr. Stehr-Green: 


Dr. Brent: 


Dr. Stehr-Green: 


Dr. Brent: 


Dr. Stehr-Green: 


Dr. Davis did a very nice job of going back and showing 
that at least for some of the major outcomes, that the initial 
information on the electronic records were very closely 
supported by more detailed clinical follow up, I think there 
is still a major issue of is a case of ADD a case of ADD 
everything, at least as ascertained, over this time period? 

Then finally I think as Dr. Rhodes pointed out that the 
exclusion criteria may have introduced other biases that 
have altered our ability to draw inferences from this data. 

One of the things, I think we might allow a couple minutes 
of discussion to clarify some of those points. I am not sure 
I understood. Are you voting yes or no? 

Voting yes, the implications are so profound these should 
be examined further. 

So the reason for voting yes was sort of a show of problems 
rather than the reasons we should pursue it? You gave 
limitations of the data rather than explaining why you think 
we should conduct further investigations. Unless you have 
one basis reason, which is not the data, but the implications 
of the data. Is that right? 

Yes, and I guess what I wanted to talk about were those 
facets that... 

The problem being with the data, is that right? They don’t 
really explain why you think it should be further pursued. 
The main reason that you think it should be further pursued 
is? 


The implications and if further research is done, I hope that 
it can somehow rest these concerns or mitigate these 
concerns. 


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Dr. Brent: 

Dr. Stehr-Green: 

Dr. Johnson: 

Dr. Stehr-Green: 
Dr. Johnson: 

Dr. Sullivan: 


Dr. Johnson: 
Dr. Clarkson: 


So the reason for further investigation is not really from the 
data themselves? It’s not in the strength of the data? 

Not on the strength, no. They are intriguing, but certainly 
not compelling. 

Paul, some of what you said might fit under question two? 
Yes, all these questions were kind of inter-related. 

Kevin? 

I said yes. In my mind it appears there may be a small 
possibility of some increased risk. I am not convinced that 
there is, but the question was do we stop and not do 
anymore work, or should we go on and do some further 
investigating? I say that there should be some additional 
investigation into the potential association. 

That seems clear. Dr. Clarkson? 

I said yes, too. I am not quite as enthusiastic. I only heard 
Dr. Weil’s comments, but I was giving the same reason that 
maybe some additional observations could be made. For 
example, some of the non-mercury endpoints could be 
looked at. Again, I come from a long line of researchers. I 
hate to say no to stuff in research. 

The point I think is unique from a mercury point of view in 
that there is an astronomical number of people in this 
study. All previous mercury research has involved 
epidemiologically groups of less than 1,000 infants. To go 
from 1,000 to 100,000 is a staggering jump. So I am 
fascinated by the site of it. 

Now if you take out the Faeroes or the Seychelles, although 
they disagree as far as prenatal outcomes are concerned. 


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they are in agreement in terms of postnatal outcomes. All 
indicate that they cannot find any fact due to the postnatal 
exposure. For example in the Faeroes, they looked at the 
kids at 12 months of age and found that the higher the 
mercury levels in these kids, the more rapidly they obtained 
the developmental milestone. If you recall, they 
confounded or they suggested it was breast feeding. There 
is a lot of the breast feeding theory. The higher the 
mercury level in the kid, presumably breast milk being the 
source of the mercury, and of course the benefits of breast 
feeding. So what they found in the first 12 months was 
they could not find an adverse effect. 

At six months and at 19 months in the Seychelles, we 
couldn’t find anything either. And in Iraq where we looked 
at kids with astronomical blood levels, up to 1,000 parts per 
variant in blood, well experienced pediatricians as a team 
could not find anything obviously wrong with these kids. 
So the recurrent body of evidence says that postnatal period 
is not the window of susceptibility. As Dr. Brent 
mentioned yesterday, it is probably to do with 
neuromigration, which is in an earlier part of gestation. 

On the other hand a point I have said before, that these 
studies are 1,000 or less and here we have 100,000 infants, 
so as a mercury manic, to make me say yes, let’s keep 
looking at this group, it’s a very large group. 

A third reason for us to continue is that it might be a guide 
to future studies. I don’t know whether future studies are 
possible, given that maybe mercury in vaccines is coming 
out now, and maybe not in this country, but elsewhere. It 
might guide us to what other additional things you could 
look for in a future study. For example, the role of breast 
feeding probably is very important in determining these 
outcomes. And of course you can’t get it in this particular 
study, but maybe in a future prospective study you could 


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Dr. Johnson: 


Dr. Rapin: 


Dr. Johnson: 


Dr. Bernier: 


Dr. Johnson: 


Dr. Oakes: 


look at that. So these are my reasons and I expect to get 
10% of the budget. 

By the way, my understanding, the current understanding is 
that neuromigration incurs even in adult brains, and that 
this has been shown in animals and it has changed the 
whole concept of spasticity. 

But we are not talking of the migration which results in the 
organization of the cortex, and the amount of migration is 
small and it is horizontal and not vertical. It is a 
completely different phenomenon. 

It is not the same thing, but I don’t think it is correct to 
assume that there is not a whole lot going on in the central 
nervous system from the time of birth on. 

I mean compared to what went on in the embryo, I think it 
is miniscule. All the cells that make the neuron come from 
that single cell layer, the appendum of the brain. They are 
gone. They are not there anymore in the adult. You can’t 
form any new cells from the neuroplast. So what you are 
talking about is an interesting phenomenon, but we don’t 
know of its implications with regard to behavior or 
learning. 

There is a lot of study in that area. Bob. In any case, we 
don’t need to get into this. David? 

With regard to the first thing Tom said, in 30 years I don’t 
hear everything and any group of experts addressing any 
topic when the group has felt comfortable at the end of the 
meeting, saying we know everything we need to know 
about this. Let’s not do anymore research. It would really 
go very much against the grain to take a no on one. And 
that is not related to the strength of the evidence. It hardly 


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matters. Actually the methodological issues and the 
interest in the topic. 

Also I think things could be done, further analyses of the 
data, further confirmation of some things and not at great 
cost, that would help clarify at least some of the issues 
involved. 

Dr. Bernier: One of the reactions I am having as I am listening to this, I 

agree with you completely about this 30 years and never 
expecting scientists to say that they don’t want to do more 
studies. That more studies would be good. So I am 
wondering why question this? We knew the answer, so let 
me try to defend the question a little and if you agree, 
maybe we could start over again. 

The point I am making is that the way this question was 
written is not do we need to know more about mercury? 
The question is really do you think that the observations 
that have been made in this project are of sufficient concern 
that you want to investigate more the relationship between 
the vaccines which contain mercury and these outcomes? 
So it is not just a question of do we want in general? It has 
to do specifically with that issue. Is the level of concern 
that you have about it, has that been raised enough by what 
has been observed that you want to investigate more that 
specific question? I don’t know if that is the same thing. 

I think, Roger, that is the same question. I think perhaps 
what is a better question, is what is your level of concern 
about these findings? 

Well, that really is the second. 

Well, the second issue is we don’t know causality. We 
don’t know about causality, but is this something that really 
warrants some urgent attention? It is two issues as to what 


Dr. Orenstein: 

Dr. Bernier: 
Dr. Orenstein: 


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is your level of concern and the need to look further in 
terms of concern, whereas I don’t know how the people got 
causality. But there may be a different issue as to whether 
they are willing to admit to how strong plausible versus this 
is something we need to worry about and we need to do 
more on it. 

Dr. Johnson: Roger, would you like for us to grade this as 1+ or 4+ so 

you don’t get too fine a cut in terms of concern. Would 
that introduce... 

Dr. Bernier: Let me stop for a minute because I am trying to think about 

the point that you are raising, Walter, and it seemed that it 
wasn’t helpful to just hear about the level of concern 
because to interpret that, it could have multiple 
repercussions. It could mean that it is concern, therefore 
that concern needs to be translated into a policy action or it 
means that the concern is that you don’t think the evidence 
is strong, and therefore it is not worth doing more research. 

I mean just to measure people’s level of concern without 
trying to get a handle on what does that operationally 
mean, I don’t think is really helpful. So the reason we put 
this question this way was to operationalize what was 
meant by the signal. And likewise by the second question, 
it was to operationalize it by expressing it in terms of what 
you thought about how much this supported a causative 
relationship or not. 

I don’t know if others have different views and I don’t 
want to get into a big semantic debate, but on the other 
hand I don’t want to wind up after the meeting and people 
feel well, we could have fine tuned what we were doing 
and it would have been a little more helpful. This is a rare 
opportunity we all have to be together this morning to hear 
one another on this, so we want at the end to feel that we 
got the most out of this. So Susan, do you want to make a 
suggestion? 


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Dr. Ellenberg: 


Dr. Bernier: 


Dr. Ellenberg: 


Dr. Orenstein: 


Dr. Bernier: 


Mr. Schwartz: 


I may be jumping the gun, but one of the ways you could 
frame it is the level of concern sufficient to have an urgent 
and immediate research plan to address the question. And 
the other one is the level of concern sufficient to instigate a 
chain of policy? I know that’s jumping. The best way of 
measuring the magnitude of concern as opposed to 
measuring it related to causation, which I don’t think 
anybody would be able to say that they know. 

They don’t have to know, they just have to render. The 
way the question was written is that you render an opinion 
about the evidence as it exists. Does it or does it not 
support a causal relation? It is not a yes or no question, it 
is just that how much do you feel it does support it? 

But I think in terms of quantitative concern would at least 
may be able to determine what kind of action you can take. 

I think you are talking about two qualifications. One is 
what is the level of concern of the need for action? I agree, 
I think I would be shocked if everybody went around the 
room and said I’m just not sure. May be, but I think the 
issue is what is the level of concern. 

But what will that mean, Walter, if after this meeting 
everyone goes around and says I have a level of concern 
and it’s high. What are you going to do? 

Can I just make one quick suggestion? In the past you 
asked the question how strong are these data as a signal? 
That might be one question. What do you think of these 
data as a signal of a problem? 

The second question might be what is your level of 
concern, and concern brings into account the signal, but it 
also brings into account all of the expertise that these 
mercury folks have given us, and these developmental 


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folks about concern regarding not just the signal, but the 
issue in general. 

And the third question is what do we do about it and is 
more research needed and how urgent is that research? So 
if you are trying to separate what^do you think of these data 
from what do you think of the issue, that might be one way 
to do so. 

Dr. Oakes: The other side to this is these data are now out. I mean 

they may not be public, but they will be. So this data 
exists, and then we can’t go back to the state where this 
duty has not been done, so there is a need to understand the 
data we have. And that might be the way I would frame it. 
A better understanding of the results that we have. 

Dr. Clover: Maybe that is an impossible question to answer, your first 

question, because no one around here is going to say that 
mercury per say is not a concern. 

Dr. Clarkson: Thank you. 

Dr. Stein: Let’s go on around on the first question. 

Dr. Weil: I may have helped or not. My answer is yes. Although the 

data presents a number of uncertainties, there is adequate 
consistency, biological plausibility, a lack of relationship 
with phenomenon not expected to be related, and a 
potential causal role that is as good as any other 
hypothesized etiology of explanation of the noted 
associations. In addition, the possibility that the 
associations could be causal has major significance for 
public and professional acceptance of Thimerosal 
containing vaccines. I think that is a critical issue. 


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Finally, lack of further study would be horrendous grist for 
the anti-vaccination bill. That’s way we need to go on, and 
urgently I would add. 

Dr. Brent: Well, I have to preface my answer, which of course is yes. 

First of all, and I know others have said this, I have been 
very impressed with the presentations we had yesterday. It 
is not only the quality of the presentations, but I think the 
objectivity of the investigators. They really presented 
every aspect of the possibilities of it being a finding that is 
not causal versus one that was, and I think that is 
important. 

As far as the answer to the question, I think it is not only 
one of further investigation, but what further investigation? 
With the birth defects, we have five areas that we look at 
when somebody alleges that something is possibly causal. 
One is what we discussed here today. Epidemiology. In 
our field it has to be consistency. In other words, we never 
depend on one epidemiological study because of what I 
mentioned yesterday, that if you look at enough T-tests, 
you are going to come up with a positive with relation to 
one birth defect. Therefore, you had better have that same 
birth defect come up in the next epidemiological study and 
the next one. 

The second thing, the secular trend. I am impressed with 
the fact that some people here have information and believe 
that like the incidence of learning difficulties, behavior 
disorders and attention deficit is increasing in our 
population. I don’t know whether it is or it isn’t, but that 
kind of information you just can’t throw around and say it’s 
true or isn’t true without data. And it is such an important 
area in our society. I mean it is the thing that makes a 
human being different from the other species, so it is such 
an important area of research. 


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The third area, one that we depend on a lot, is animal 
studies, and the fact is while you can’t predict without 
knowing from a human study what you are interested in, 
the animal studies can be very helpful in looking at the 
mechanisms, thresholds and the incidence of the findings 
that you have in the human. 

Then the fourth area is pharmacokinetics, which I think is 
crucial in this particular area, and the fifth is biological 
plausibility, and that’s how we evaluate whether something 
causes birth defects or not 

So we are stuck with just Epidemiology here today and I 
think from the standpoint of further research, we need to 
find out whether these increasing dosages of 
methylmercury are really increasing dosages on the basis of 
pharmacokinetics or whether the fact is that each injection 
is a separate dose unrelated to the other one. I think that 
has to be done. 

So what I wrote is that the results of the study that was 
presented reports a statistical association between vaccine 
exposure and certain neurological deficits. Two of the 
three explanations for the findings relate to patient 
selection problems and one explanation relates to exposure 
to the vaccine. 

The incremental exposure to methylmercury. Statistical 
associations and causal connections are strengthened by 
obtaining the same results in a second or third 
epidemiological study, therefore, this should be pursued 
with appropriate populations. 

Biological plausibility should be studied by performing 
pharmacokinetics in humans to determine the biological 
half life of ethylmercuiy in the blood levels of 
ethylmercury following administration. Appropriate 


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animals models utilize the ethylmercury in the threshold for 
neurobehavioral effects in each species should be initiated. 

I think that is the basic data you would need to begin to get 
a handle on this problem. 

Finally the implications are profound. I remember when I 
was an intern, I rotated to Boston and there was a woman 
there by the name of Pricilla White. Because I had been a 
researcher before I was an intern, she would come down 
and show me these placentas from mothers who were 
diabetic and because they were using DES, and she would 
say to me look at that placenta. Look how healthy it is 
from mothers who are on DES. Of course she was 
eventually crushed psychologically when they found out 
that it caused adenocarcinoma of the vagina. And the 
implications here are much vaguer. That was an epidemic 
which was horrendous. Causing learning disabilities and 
behavioral disorders. ADD is a tremendous problem in our 
society and I think it is one that we should be very 
concerned about. 

Although my gut reaction, which is totally irrelevant, is 
that it probably is not causatic, the only way you can come 
to a conclusion is through the data, and that’s the data I 
think we have got. Even if we put the vaccine in single 
vials and put no preservatives tomorrow, we still want the 
answer to this question. Because remember, 
epidemiological studies sometimes give us answers to 
problems that we didn’t even know in the first place. 
Maybe from all this research we will come up with an 
answer for what causes learning disabilities, attention 
deficit disorders and other information. So I am veiy 
enthusiastic about pursuing the data and the research for 
solving this problem. 


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Dr. Johnson: 

Dr. Brent: 
Dr. Johnson: 
Dr. Roller: 


Finally, the thing that concerns me the most, those who 
know me, I have been a pin stick in the litigation 
community because of the nonsense of our litigious 
society. This will be a resource to our very busy plaintiff 
attorneys in this country when this information becomes 
available. They don’t want valid data. At least that is my 
biased opinion. They want business and this could 
potentially be a lot of business. 

Thank you. Bob. I think you will agree that biologic 
research also needs confirmation, even when there is a hard 
Biochemical influence. 

Absolutely. 

Okay, Loren? 

In order to adequately answer question one, I took the 
prerogative to break it into two questions. The second 
one will answer your part of it. 

Part one, is there a causal association between 
ethylmercury and neurological effects noted in the Vaccine 
Study Datalink project? The answer is no. Why not? 
From a toxicologists viewpoint there is no dose response 
relationship in some of the effects, particularly if you look 
at slide 18 where the cumulative mercury exposure, the 
rates for speech delay and ADD. At several doses those 
were lower or equivalent to the zero exposures for each one 
of those categories. 

Another reason, in risk assessment the best human data is 
followed by the best animal data and it is used to determine 
no-L’s and low-L’s health affects. 

Uncertainty increases in the direction from humans to 
animals, from high quality to low quality data or the lack 


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thereof. In my opinion the Seychelles study contains high 
quality human data, so that is the data that you use and it is 
supported even by the Faeroe studies and other studies in 
humans. The reason, there are 711 mother/infant child 
care, very few confounders. Children were exposed to high 
levels of mercury in utero, neonatal, during development of 
the nervous system, the most sensitive time. The children 
were vaccinated. There was continuous exposure 
throughout compared to single exposures in this situation. 
There were no adverse health effects in six neurobehavioral 
tests. As a matter of fact, in the higher group they scored 
higher on four of those six tests. Albeit, recognizing that 
there are other tests that may be more sensitive to detect 
neurological function. So therefore in my opinion there is 
no evidence that childhood vaccination would attain or 
exceed the Seychelles mean hair or blood mercury levels, 
let alone fourfold higher at the maximum range in that 
study. 

So part two. Are the observations of sufficient concern to 
warrant further investigation? Answer is yes. Some of the 
neurological developmental disorders show a small, but 
suggestive increase in relative risk. 

Dr. Johnson: Loren, were you answering question two? 

Dr. Koller: No, I have question two as similar, but a little different. 

Bob’s first statement I think sort of laid it on the line. As 
you increase the vaccination, you increase effects, but you 
don’t know. You have modified live viruses. You have 
different antigens. There is a lot of things in those 
vaccinations other than mercury, and we don’t know 
whether this is a vaccination effect or a mercury effect. 
But I am almost sure it is not a mercury affect. Positive as 
a matter of fact, and there are several experts particularly 


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Dr. Johnson: 

Dr. Koller: 


Dr. Johnson: 

Dr. Koller: 

Dr. Clarkson: 

Dr. Brent: 

Dr. Clarkson: 
Dr. Johnson: 


that have reviewed this, the methylmercury aspect who I 
think would agree with that due to dose response. 

Are you really comfortable with the way neurologic 
function was tested in the Seychelles? 

I have to admit that there were many other tests that could 
have been conducted. In any of the mercury human 
exposures that have observed neurologic findings, most of 
them are negative clinically. We are talking about very 
subjective, very sensitive assays and yes, there could have 
been others done and there should be more done. That’s 
part of number three. But we have to use the data that is 
available. If we went back to animal data, when you talk 
about suggestive and sensitive tests for neurological 
function in humans, it is much more difficult in rats and 
mice to detect those changes. 

Can’t you put them out on those little floating pads and see 
if they swim and how fast they go through mazes? 

You can, yes. In my opinion that is not quite as sensitive 
as it in humans. 

Can I comment on that for a second? On the animal 
experiments. There is a lot of literature of animal data on 
methylmercury, and quite a lot on primates as well. The 
level of effect, the lowest effect level in those animals is 
about 100 times higher at least than what we are talking 
about in the Seychelles or the Faeroes or here. 

But that is with methylmercury. 

Yes, methylmercury. 

That’s on a wave basis. 


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Dr. Clarkson: 


Dr. Johnson: 


Dr. Roller: 


Dr. Clarkson: 


On brain levels, too. If you convert them to actual brain 
levels, you are talking about estimated brain levels of about 
100 times higher. I agree that the animal data is useful in 
terms of mechanisms, in terms of what stage of gestation is 
important and so on, but I don’t think that you are going to 
get human risk levels out of animal experiments. Because 
probably as you say the kind of tests you can do on an 
animal is not the same tests that you can do on a seven year 
old kid. 

Loren, if you are absolutely sure there is no causal 
relationship, why would you answer yes to question one? 

Because I think there are other factors. There is many 
confounders that have not been evaluated. Biological and 
environmental. As a matter of fact, in question two one of 
my answers is there does appear, however, to be a weak 
positive association between increased numbers of 
vaccinations and some neurological endpoints. That is 
shown on slides 21, 23, 24 and 25. Because as you 
increase mercury, you increase vaccinations, so there could 
be several other factors in those vaccinations that are 
causing these effects. 

There is also other types of vaccines that these children are 
exposed to. There might be a combination biological 
effect. It might be antigen effects. There is all kinds of 
possibilities here. Some of these are modified life viruses. 
I would assume they are modified life viruses. Something 
between the combinations or subsequent exposures in a 
sensitive population, or hypersensitive population may 
trigger some of these effects. 

It will be interesting, Mr. Chairman, to know the 
conclusion of the aluminum meeting in Puerto Rico. What 
came out of that? Because we heard yesterday from the 
CIs that aluminum will correlate just as well as mercury 


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Dr. Myers: 


Dr. Clarkson: 
Dr. Myers: 


Dr. Stein: 


with these results. Is Dr. Myers here? What were the 
conclusions? 

Well, first we didn’t have this data to study. We didn’t 
have available what we are discussing today. This study, 
so I am not sure. 

What did they reveal about the all aluminum in terms of... 

They thought there was an enormous margin of safety, that 
were well below concerns, but again they hadn’t seen these 
associations. By summary we thought we were well below 
the mercury as well. 

Well, of course I answered yes also, but first I want to say 
thank you to everyone for giving me a course in 
Epidemiology. I learned a lot. I want to also congratulate 
the group that did the study and the data analysis. It also 
gave me a great respect for the problems of evaluating 
vaccine safety beyond what I had ever known or expected 
before, and obviously I have been practicing pediatrics for 
a long time. 

But I said yes because there are a lot of issues raised here. 
From my point of view as a clinician, it is not the subtle 
statistics that have been discussed and which are important, 
but really the endpoint. And that is the quality of these two 
diagnoses that have fallen out, attention deficit disorder and 
speech and language delay. 

I recognize the limitations of a study like this, but I am 
going away uncertain whether these children, or most of 
these children, or a significant number of these children, 
really had ADHD or really have speech and language 
delay. I don’t think the way the study was set up, even 
with the chart review, we really haven’t been told about the 
quality of the diagnosis, the tests that were used to 


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Dr. Brent: 
Dr. Johnson: 
Dr. Rapin: 


substantiate these two diagnostic categories or the quality 
of the people doing the tests. In the area of 
neurobehavioral and neurodevelopmental problems, those 
factors are veiy important and it seems to me we are 
putting so much value on those outcomes without being 
able to substantiate. It is not like doing an SGOT where 
you can control for the quality pretty well. You can control 
for the quality of neurobehavioral and neurodevelopmental 
evaluations, but you have to have the knowledge to know 
how they were done, and we don’t know that in this study. 

Perhaps we could get better information by reviewing the 
charts in a different way, and for that reason 1 would vote 
yes. That we need to know about this, but I don’t think you 
can make any conclusion that mercury is related to ADHD 
or speech and language problems in these children, given 
the lack of information about the quality of the diagnosis 
and that is your endpoint. 

So what studies would you suggest? 

That’s another whole thing. Dr. Rapin? 

I voted yes, but I had a question mark. I guess ayes 
question mark. I kept erasing and putting back in. I erased 
it finally this morning, but it was there. The question mark 
was because I was not at all convinced that the exposure 
level had reached a significant threshold effect after what I 
heard yesterday about mercury exposure. 

Secondly, I was very impressed with the Faeroes and 
Seychelles, especially the Seychelles Island studies in 
which the children had much higher levels and no effect 
was detected. 

I also felt that the study which we were provided on the 15 
infants, five of whom were full term and 10 premature, was 


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strong because of the very smallness of the data set. So 
these are the reasons I have this question mark that kept 
wanting to come back. 

In terms of why did I think we should pursue this. Well, as 
has been said by others, the first was the data that are there, 
they won’t go away. They are going to be captured by the 
public and we had better make sure that (a) we counsel 
them very carefully and (b) that we pursue this because of 
the very important public health and public implications of 
the data. 

I felt that the evidence, although statistically significant, 
the magnitude of the effect I thought was small and I was 
somewhat reassured by the chart review, and I really 
wanted to commend the reviewers because I have done a 
lot of chart reviews. It is a lot of data. But nonetheless, for 
reasons I will put in some of the later questions, I felt that 
the measures of attention deficit and language disorders 
and so on were weak. I have other criticisms that I put in 
the new methodology. 

But again I want to thank you for this opportunity to review 
these data. 

Dr. Johnson: Thank you, Isabelle, I don’t have anything substantive to 

add. I of course voted yes. There were two reasons. The 
stakes are very high and Bill Weil made this point. Any 
** detrimental effect on infancy is serious enough to warrant 

as strong as possible efforts to define the relationship. 

The second reason, as Bill also noted, despite of numerous 
efforts, and I agree with Bob Brent, I was impressed with 
the open mindedness and the concern in trying to ferret out 
what the relationship really was at all costs that was 
exhibited by particularly Tom, but also Phil Rhodes’ 


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analysis, so in spite of that there is still some worries. So I 
put down for those two reasons a yes. 


Dr, Chen: 
Dr. Bernier: 


Dr. Johnson: 
Dr. Rapin: 


Dr. Johnson: 


Any other comments on that question alone before we 
move on to question two? 

Roger, do you want to read Alex Walker’s? 

Alex Walker voted yes and he said if yes, why? You had a 
prior concern. You obtained mostly negative findings, but 
some positive results. If you do not treat this as a signal, 
other than much less responsible parties will do so, and 
follow up will be out of your control. Equally, the negative 
findings need to be pinned down and published. I think 
that is published. Need to be pinned. The negative 
findings need to be pinned down and published. 

That’s very pragmatic. 

Can I make one comment about the business of the 
increasing prevalence of developmental disorders? I think 
that this parallels increasing education and sophistication of 
people who examine children. I can tell you from my own 
experience that 20 or 30 years ago I barely diagnosed 
autism unless it was so blatant that it stared me in the face, 
and now I see at least two new ones a week. And not so 
severe as the previous ones, so I think there is a tremendous 
change in the threshold of ascertainment. And yes, I have 
seen the California statistics which says it has increased 
300 fold, but I would interested to know whether it has 
increased 300 fold in areas where there are physicians who 
have been trained in this recognition, as opposed to areas in 
which there are not. 

Thank you. We’ll go to question two and go back in a 
reverse direction. The question is* if you think the 
observations on some specific neurologic developmental 


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disorders constitute a signal, how strong or weak do you 
consider the signal to be at this time, i.e. how much does 
the evidence support a causal relationship with Thimerosal 
containing vaccines? 

I assumed this a number one. In my opinion the evidence 
today is insufficient to determine whether or not 
Thimerosal containing vaccines caused the neurological 
sequelae in question. 

The diagnoses, even in the hands of experts, and the 
number of diagnoses are too easily influenced by variations 
in parental and physician sensitivity and concern, 
utilization of health care of similar merits. 

The underlying biologic, toxicologic and pharmacologic 
data are too weak to offer guidance one way or the other. 
That is the biologic plausibility component of this, in my 
opinion, is too badly defined. 

Now on the other hand, the data suggests that there is an 
association between mercury and the endpoints, ADHD, a 
well known disability, and speech delay as entered into the 
database. 

Then here comes an opinion, well it is all is opinion, but it 
expresses a flavor, so I think it relates to what Dr. Bernier 
is trying to derive here. This association leads me to favor 
a recommendation that infants up to two years old not be 
immunized with Thimerosal containing vaccines if suitable 
alternative preparations are available. I do not believe the 
diagnoses justifies compensation in the Vaccine 
Compensation Program at this point. 

I deal with causality, it seems pretty clear to me that the 
data are not sufficient one way or the other. My gut 
feeling? It worries me enough. Forgive this personal 


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Dr. Rapin: 


comment, but I got called out at eight o’clock for an 
emergency call and my daughter-in-law delivered a son by 
C-section. Our first male in the line of the next generation, 
and I do not want that grandson to get a Thimerosal 
containing vaccine until we know better what is going on. 
It will probably take a long time. In the meantime, and I 
know there are probably implications for this 
internationally, but in the meanwhile I think I want that 
grandson to only be given Thimerosal-free vaccines. 

I hesitated between a one and a two. I finally put in a two. 
My first statement was I thought there was an association, 
but it was not clearly causal. I felt that some of the things 
that made me feel this was weak was that children were 
counted as cases, irrespective of the age of diagnosis. As I 
said yesterday, many children who speak late turn out not 
to have language disorders, so there was no opportunity in 
the study for any change in diagnosis. 

I felt the children were all studied below the age of six 
years and that attention deficit disorder, with or without 
hyperactivity, is an extremely weak diagnosis in pre-school 
children. 

I felt that the diagnoses were made at different ages and the 
length of follow up varied, so that some children were only 
followed for a brief period of time. Those bom in ’96 and 
’97 were really seen for a very brief period of time. 

I felt that even though some of the children were confirmed 
by referral to agencies for confirmation or for treatment by 
chart review, there was a lack of confirmatory tests. 

I felt that the fact of parental wony on both detection and 
referral were important confounding variables. 


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I felt that the premature data which went in the opposite 
direction I found very troublesome, and finally the lack of 
family history data which would reflect on genetics, which 
I think are most important than environmental effects in all 
of the developmental disorders, was weak. 

Dr. Johnson: Thank you. Dr. Stein? 

Dr. Stein: Well, I also gave this a two to answer the question. My 

main reason was that the outcome measures of 
neurodevelopmental disorders do not provide enough 
specificity to make the diagnosis, as I said before. Again, 
we really don’t know the quality of the diagnosis, and I will 
get into that in a moment. 

Secondly, genetic influences were not considered. We 
need to know more about the family history, and when we 
get to the third question I will make a suggestion for that. 

Three, there was a limitation. It occurred to me that the 
parents who take their kids for Hepatitis-B vaccine, 
especially in the early nineties when it was first 
recommended and at that time the conjugated HIB came 
out to give in early infancy. When was that? Ninety, so 
that’s when infants received it for the first time because 
before that we were only giving it at 18 months, 24 months. 
These parents who knew more about the vaccine and might 
have accepted the vaccine may have been more sensitive 
parents and more sensitive to medical information in 
general, and more sensitive to developmental variations in 
their children. They may have raised more concerns during 
health supervision or well child visits, and requested 
evaluations for ADHD and speech delays. 

Another aspect with regard to the introduction of Hepatitis- 
B is that when it was initially recommended by the bodies 
at the CDC and the American Academy of Pediatrics, many 


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pediatricians around the country were uncomfortable with 
that diagnosis because they had never seen a case of 
Hepatitis-B and wondered whether that was really an 
appropriate vaccine. And the question is were these 
pediatricians who gave the Hepatitis-B earlier more likely 
to be those who read more about it and also likely to be 
reading more about developmental delay and be more 
sensitive to that diagnosis. It is a hypothesis, but it 
certainly could affect the results. 

Next, there is really no systematic review of the actual 
diagnosis of speech and language delays. I spoke a little to 
Tom at breakfast, and Isabelle has raised some of these 
questions about the maturational effects, particularly 
expressive language delay in boys at two to three years of 
age and how this can, in fact, be maturational in the 
majority of cases. Then when you evaluate them at four 
and five they don’t have a speech defect. Eventually some 
may have learning disability pertaining to reading 
problems, but there is a lot of fluctuation to that diagnosis. 

This is subjective. This is not the data that Tom pulled 
from the charts quantitatively, but many of the speech and 
language diagnoses were mild articulation defects, or 
articulation defects which are usually mild when they come 
from a general Pediatrician, and often reflect the greatest 
sensitivity of parents and concern and anxiety about 
parents, with what I would consider a developmental 
variation and not a true disorder. Whereas the speech 
pathologist may code it as an articulation deficit and give it 
a code, a diagnostic code. In fact, many of these are 
developmental variations. Just as the maturational 
expressive language delay in many cases at two and three 
years of age is a developmental variation. These aren’t 
really disorders. And again, we are basing these results on 
these 1,533 children with speech and language disorders. 


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Dr. Brent: 

Dr. Stein: 

Dr. Brent: 
Dr. Stein: 
Dr. Brent: 

Dr. Stein: 


To answer your question which relates to this, Bob, about 
what tests would I use. There are certainly standardized 
tests to evaluate expressive and receptive language and 
articulation in early childhood, and certainly through the 
pre-school period. As well as for ADHD. There is 
standardized behavioral tests that can be used. With that in 
mind, it seems that at Group Health most of these children, 
if not all, were referred to a specialist. Or at least a 
Pediatrician who concentrated on working with children 
with ADHD specifically. Now we can assume, although 
we don’t know, that person was really good at it and used 
standardized tests. On the other hand at Northern 
California Kaiser I am told, they don’t have a specialized 
clinic for evaluation children for school under-achievement 
as a broad category and specifically for ADHD. So these 
children were probably diagnosed by Pediatricians, or 
perhaps in some cases a neurologist or a child psychiatrist. 
But again it is so heterogeneous we don’t know the quality 
of that diagnosis as well. 

Have you ever seen a child who has had that diagnosis who 
when you saw the child you refuted it or didn’t support it? 

Yes, many times. 

My oldest son... 

In fact there is some data on that. 

✓ 

My oldest son happens to Chairman of Child Psychiatry at 
the University of Pittsburgh and he says about 25% of the 
children with that diagnosis do not have it, when they are 
fully evaluated. 

Right, that would be what I was going to say, about one- 
third, and there are some data. I would agree with that, 
because ADHD is a diagnosis where the behavioral 


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symptoms overlap with a variety of other conditions, as 
well as with normal variation depending on the age, and 
that is another point. 

The mean age of diagnosis of ADHD in this study was 49 
months. Four years, one month of age. Well, ADHD is a 
very difficult diagnosis to make in the pre-school period. 
In fact, in our guidelines published by The Academy of 
Pediatrics, we limit the recommendation to the six to 12 
year group because that is where most of the data is. There 
is very little firm data on the diagnosis of ADHD in the 
pre-school period. It certainly can be made, but in general 
it takes someone with lots of experience to do it, because so 
many of the behaviors of ADHD overlap with normal 
behaviors in this age group. Hyperactivity, impulsivity, 
inattentiveness. The developmental variation curve and the 
disorder curve really overlap tremendously and it takes a 
lot of experience to recognize it early on. Forty-nine weeks 
is very early. 

Finally, and this is a question that was implied. Could the 
intake of fish by mothers who were breast feeding have 
influenced mercuiy levels in this study? We didn’t look at 
the breast feeding issue. Now I assume from what you told 
us, it is known that mercury does excrete into the human 
breast milk. That is another very interesting factor I find 
that would need evaluation and further studies, but my 
main concern is again the endpoint. The quality of these 
diagnoses and all of our discussion is based on that. I think 
it kind of a weak foundation right now from what we know. 

Dr. Johnson: Dr. Koller? 

Dr. Koller: I gave it a one. First, as I indicated for question one, there 

does not appear to be a causal relationship between 
ethylmercury and neurological disorders. 


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Dr. Johnson: 
Dr. Brent: 


Dr. Rapin: 
Dr. Brent: 


Secondly, however, there is a weak positive association 
between increased numbers of vaccinations and some 
neurological endpoints. 

Third, analysis of data has not included all confounders, 
including biological, environmental, as well as genetic 
differences. 

And fourth, there is two to threefold differences in outcome 
repeated diagnosis between the two data sets, which is 
disturbing, the hyper diagnosis and interpretation of 
neurological disorders. 

Dr. Brent? 

I personally want to congratulate Dr. Johnson on his 
grandson. I have a small series of 11 children, all who 
received the Thimerosal vaccine and they are all geniuses 
of course. But as Dr. Rapin points out, the genetics was 
probably most important. 

My grandchildren are geniuses, too. I have two. 

Well, I circled one and I wrote the following. The 
epidemiological data is valid, as is the associations that 
were reported. 

It is more difficult, if not impossible, to refute a causal 
association based on this study. Therefore, the question of 
causal association remains unanswered until we obtain the 
data that was suggested in the answer to the first question I 
wrote. 

On the other hand, massive case control studies are 
sensitive, but frequently uncover non-causal associations, 
at least in our field. This wold be much better if it were a 
100,000 cohort study where you had controls and 


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exposures rather than a massive case control studies. You 
know it depends on what you pick as your controls, 
whether you end up with a positive association or not. 

The most important information in the eyes of the 
epidemiologist is if the incremental exposure to the two 
categories of neurobehavioral effects that were likely to be 
effected, had increased relative risks. But when the 
pharmacokinetic data is evaluated, at least with regard to 
ethylmercury, the results may or may not support the 
incremental exposure. 

Furthermore, the level of ethylmercury are in the range of 
mercury levels found in other populations as 
Dr. Koller referred to, where there are neurobehavioral 
findings and they didn’t receive the vaccine. 

Finally, the animal methylmercury data indicates a 
threshold for neurobehavioral events at a much higher level 
as mentioned before. This has to be determined for 
ethylmercury. So again, it is more data we need in other 
areas besides epidemiology. 

By the way, I changed the question that I answered as has 
everybody else. The question I answered was, if you think 
the observations on some specific neurological 
developmental disorders to be valid, how strong or weak do 
you consider the data to be at this time? How much does 
the evidence support a causal relationship? I think that 
word “signal” bothered a lot of us because it gives you the 
feeling that you are talking about one piece of information 
and it was all the data that we looked out in those studies 
that we were evaluating. 

Dr. Johnson: Nevertheless, in regard to causality you decided a one? 

Dr. Brent: Yes. 


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Dr. Weil: I put four and I did so for a number of reasons. 

The number of dose related relationships are linear and 
statistically significant. You can play with this all you 
want. They are linear. They are statistically significant. 

The positive relationships are those that one might expect 
from the Faeroe Islands studies. They are also related to 
those data we do have on experimental animal data and 
similar to the neurodevelopmental tox data on other 
substances, so that I think you can’t accept that this is out 
of the ordinary. It isn’t out of the ordinary. 

The Seychelles Island studies, and somebody said the 
Faeroe Island studies both, were chronic exposures. We 
are not talking necessarily about chronic exposure. We are 
talking about a series of acute exposures and at one point in 
time that exposure is much greater on that one day than any 
of the Seychelles Islands. 

The increased incidence of neurobehavioral problems in 
children in the past few decades is probably real. It may be 
a group of pediatricians, it may not be. I work in the 
school system where my effort is entirely in special 
education and I have to say that the number of kids getting 
help in special education is growing nationally and state by 
state at a rate we have not seen before. So there is some 
kind of an increase. We can argue about what it is due to. 

Right. 

But there are certainly more kids with ADD and there are 
more kids with speech and language disorders than there 
have been in the past. 

With regard to ADD I would only say that I don’t think 
there is a diagnostic test. If you look DSM4, first of all 


Dr. Rapin: 
Dr. Weil: 


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they don’t even have criteria for kids under six. Second, 
they make a point that it is a label based on a constellation 
of findings and not a single test. The Conner’s and all the 
other tests have been shown to have pretty different 
validity scores. 

The symptoms, depending on whether you are a lumper or 
a splitter. The splitters put ADD with every diagnosis 
where the symptoms occur. The lumpers say that if this kid 
has condition A and ADD, we will label it A. So there is a 
lot of variation among people who make this diagnosis, 
whether they are experts or not. 

The rise in the frequency of neurobehavioral disorders, 
whether it is ascertainment or real, is not too bad. It is 
'***' much too graphic. We don’t see that kind of genetic 

change in 30 years. 

There are also a number of toxic agents in the environment 
that could have done this. You see the evidence of 
Plopirophose as a neurodevelopmental toxic, and that has 
been widely used in the last 20 to 30 years as the most 
common household pesticide in the United States. I don’t 
know how many hundreds of tons of this have been 
distributed over the rugs, carpets, dogs, cats and kids in our 
environment and it is finally being taken off the market as 
far as home use because it is a neurodevelopmental toxic. I 
think this relationship has to be investigated just as 
thoroughly as plopirophose was. 

While the data are not sufficiently robust to accept a clear 
causal relationship, the difficulties in interpretation, the 
problems with alternative analyses and so on are not great 
enough to reject the possibility of a causal relationship. In 
other words I am saying it isn’t there and I wouldn’t give it 
a five or a six, but I don’t think people would want to reject 
this and do so with the data at hand. 


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Dr. Johnson: 

You would neither accept nor reject, but you believe the 
data are not sufficient to accept or reject, but you would... 

Dr. Weil: 

It is strong enough that I put a four. 

Dr. Johnson: 

You assigned it a four. 

Dr. Rodewald: 

What is the scale level? 

Dr. Johnson: 

One to six. One is weak. 

Dr. Johnson: 

Four is across the line. You are across the line toward the 

strong. 

Dr. Caserta: 

Is the scale for how strong the signal is or how strong a 
causal association there is? That wasn’t clear to me from 
the question. 

Dr. Johnson: 

How strong the casual. This is causality. David Oakes. 

Dr. Oakes: 

I want to pick up on something Dr. Brent said. I think this 
is a cohort study because you do have a defined population 
at the outset that you are following. There is a certain 
amount of fuzziness in the definition and incomplete follow 
up and obviously the differential ascertainment, but it is 
still basically a population that is defined at the outset that 
you are ascertaining outcomes. Imperfect, but you are 
ascertaining outcomes in a defined population. 

Dr. Brent: 

I will let the epidemiologists answer y 
you about that. 

Dr. Oakes: 

So I think that should be put as a strength. We are kind of 
honing in on the weaknesses here, but that is a strength of 
it. That you do have the study in what is a pretty well 
defined population. 


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Dr. Snider: 


And retrospective cohorts. 


Dr. Oakes: 
Dr. Rhodes: 

Dr. Oakes: 


Retrospective cohorts, not prospective. 

And that information is not figured out 10 years later. It 
happened in the past, but retrospective, perspective should 
refer to the information, not that happened 10 years ago or 
it is just happening today. 

With that preamble, I gave it a two. One of the strengths, 
doing the strengths first, is that it is in my view a cohort 
study. I find that somehow the first analysis is always in 
some ways a little bit more convincing than the reanalysis. 
Assuming the analyses were presented in the order in 
which they were done, the first analyses certainly showed 
some suggested trend tests, and I was very struck by the 
fact that you see a different pattern in relation to exposure 
from the neurologic diagnoses of interest than from the 
controlled diagnoses you chose, and I assume you didn’t 
look at 27 others and reject those that didn’t fit the 
hypothesis. I trust you did not do that. 

So those are the strengths. The weaknesses. Clearly there 
is evidence of utilization bias and you presented a lot of 
evidence and a lot of discussion about that. 

I did wonder why you didn’t do more analyses which 
formerly included the potential confounding variables. You 
did have some visualization, number of shots or numbers of 
antigens or socioeconomic status. I don’t remember seeing 
analyses where they were controlled for and you tried to 
look at the additional effect of the mercury. I think it is 
almost certain that you wouldn’t see it, but I would like to 
see that analysis done. That may have caused a much more 
problematic issue in that there are going to be many other 
potential confounding factors that you do not have data on, 


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Dr. Brent: 
Dr. Oakes: 


Dr. Sinks: 
Dr. Oakes: 


Dr. Rhodes: 


and you probably won’t be able to get. At least certainly 
not on the entire cohort. 

I don’t think we have seen any evidence that the causal 
agent, if there is one, is Thimerosal and not some other 
constituent of the vaccine. * 

Could you say that again? 

We haven’t seen any evidence that it is the mercury, if 
there is some damage being caused, that these associations 
are real, that it is an association with mercury. The 
question is what other things are in there that are also 
potential causal agents? 

I am worried and I am not sure if it has been resolved or 
what the resolution is about the issue raised about the 
potentially unusual, possibly incorrect codings of some of 
the files and whether that really did have a very strong 
influence on the analysis. I’m not sure if that has been 
fully investigated or not. 

Could you... 

There was an issue that some of the codes may have looked 
very unusual for that time and may have been incorrect, 
and I am not sure whether the status of that is that they may 
have been incorrect or that they are known to be incorrect. 

What we know about some of them, apparently it is 
information that was entered as it happened. It happened 
yesterday and the day before and it is being entered. There 
are certain quality checks on the data being entered, certain 
information, like what facility, what occurred, what 
manufacturer. There are vaccines that are entered a long 
time after because of various reasons and those do not 
always have those quality checks. Most of the ones that I 


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Dr. Oakes: 


Dr. Rhodes: 


Dr. Oakes: 
Dr. Rhodes: 
Dr. Johnson: 
Dr. Clarkson: 


have seen as possibly being incorrect are these ones that 
apparently have been entered a long time after. Those are 
missing facilities, missing the manufacturer, so that leads 
me to believe that there is less quality control. I don’t think 
there was ever a check in the program that said you can’t 
possibly be getting a separate DTP because we don’t do it. 
I don’t think there were those kinds of quality checks. 

Are you in a position to say that some of the codings are 
definitely wrong? 

We are in a position to say that some of them are very 
suspicious and need to be checked. 

But it is still a question mark at this point? 

I can’t say definitively. 

Thank you. 

Well, Mr. Chairman, this is a historic moment. Two people 
from Rochester come up with the same number. I gave it 
two instead of one in a sense because I think speed delay is 
a plausible effect to a mercury compound in children or in 
infants. But I am very influenced by the pediatricians here 
who say for example speech delay is very poorly defined. 

As far as the causality side itself is concerned, if you look 
at the mercury levels, those actually quoted in the reprints 
you have and those that we can calculate from what we 
know about the pharmacokinetics of methylmercury, these 
mercury levels, even given as a single shot, are still 
substantially lower than what you see in the Faeroes or the 
Seychelles, even though it is a single shot. I think this 
emphasized the need for this group to take a look at the 
pharmacokinetics in this study. I think it is something that 
can be done. You don’t have all the body weights, but you 


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have the birth weight and you have the growth chart, so 
you can come up with reasonable numbers for body 
weights throughout the first six months. Then just take a 
look and see what these numbers are. 

The ones reported in the literature are reasonable. Given 
the whole body weight involved here, given what we know 
about the pharmacokinetics of methylmercury, these 
numbers are reasonable. So I think it would be very 
helpful to come up with estimated blood levels here, to see 
how they relate both to the Faeroes and to the Seychelles. 
And I will reiterate that both the Faeroes and the Seychelles 
agree as far as postnatal exposure is concerned, there is no 
disagreement. Both studies have not been able to find 
anything connected with postnatal exposure in infants. So I 
disagree a little with my colleague down here because he 
mentioned prenatal data, but the postnatal data, which you 
were concerned here with postnatal exposure, is consistent 
in this respect. 

Finally, I think there is some evidence that there is a 
confounder here. If you look at the correlations for 
cumulative exposure at one month, if I read this correctly, 
Tom, you were finding correlations with language and 
speech delay at the one month. To me the increasing 
mercury levels in your population at one month due to 12.5 
micrograms, is so small that it would suggest to me that 
you have a confounder here. That this is not due to 
mercury. The increase in a kid of 3.3 kilograms with 12.5 
is within the normal range. It is hardly detectable. So this 
suggests to me that if you do get a correlation here, it is 
probably due to other confounders or other causes here. 
There may be a mercury effect, but it suggests here that 
there are other effects that would explain it. As you 
yourself mentioned, that the first cause is the parents 
attitude. I agree with that. 


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Dr. Brent: 


Dr. Clarkson: 

Dr. Brent: 

Dr. Clarkson: 


Dr. Brent: 

Dr. Clarkson: 
Dr. Brent: 

Dr. Clarkson: 


Dr. Clarkson, could I ask you to elaborate this point you 
made about postnatal exposure in .the Faeroes and the 
Seychelles yield the same result. I would like to 
understand that. 

The same result is that we didn’t find anything. 

What postnatal exposures? 

The postnatal exposures in the Faeroes were levels in the 
children at 12 months of age were correlated with 
neurodevelopmental outcomes. Actually in the Faeroes the 
paper is about the 1 996 paper. It is the same cohort. This 
is the cohort where they found prenatal effects at seven 
years of age. Now in that same cohort at 12 months of age, 
a comparison was made with levels in those kids at 12 
months of age. Not in the mother. Not the prenatal levels, 
the postnatal levels at 1 2 months of age. In that report, no 
adverse effects correlated with these postnatal exposures. 
Are you with me? 

And that is controlled in some way for the prenatal 
exposures? 

No. 

That is what I am confused on. You have got two 
different.. 

The difference are no correlation with the postnatal 
exposure at 12 months of age. Now the prenatal, there was 
an effect of prenatal exposure, but that effect was picked up 
at seven years of age. So in the Faeroes study which is the 
only one that found a prenatal exposure, they could not find 
any postnatal correlation, nor could we in the Seychelles. 
We looked at kids at six months of age and 19 months. We 
couldn’t find any correlation with postnatal. 


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Dr. Brent: 

Because that is a crucial point. Everyone keeps talking 
about the difference between the study that found positive 
results and the study that found negative results. You are 
saying in fact the studies have both found a negative result 
for postnatal exposure. That is crucial. 

Dr. Clarkson: 

They did not find anything. If you find something, perhaps 
sometimes people say that’s a positive result. So we have 
to be clear about this. Both in the Faeroes study and the 
Seychelles, they were not able to find any correlations 
between measured postnatal exposure and the outcomes. 

Dr. Brent: 

What age again? 

Dr. Clarkson: 

Twelve months, and the outcomes in the Faeroes was 
attainment of the classic developmental milestone. 

Dr. Brent: 

But at age seven? 

Dr. Clarkson: 

At seven years of age there was a correlation between 
neurobehavioral effects and prenatal exposure, and there 
was no correlation at seven years with postnatal exposure. 

Dr. Brent: 

Postnatal, and the neurologic exams in the Seychelles were 
done? 

Dr. Clarkson: 

At six months, 19 months, 29 months and 6 years. 

Dr. Weil: 

There is also a 96 month. 

Dr. Clarkson: 

We haven’t published that yet. 

Dr. Weil: 

But there is one? 

Dr. Clarkson: 

There is one, yes. They are in the hands of the statisticians. 
They are physically doing it. 


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Dr. Weil: 

Dr. Clarkson: 
Dr. Weil: 

Dr. Clarkson: 


Dr. Weil: 

Dr. Clarkson: 

Dr. Weil: 


But it is also very difficult to determine the postnatal 
exposure levels, because nobody measured how much 
mercury they were taking in every day for seven years. 

That is correct. 

So the postnatal data is very worrisome in terms of what 
the actual exposure was. In addition, the sensitivity of the 
evaluation is not what we would have hoped in terms of if 
we do these kind of data before that had happened, we 
might of looked at somewhat different. 

The problem with the Faeroes for instance is that they were 
getting actually beneficial effects. So that in terms of the 
attainment of the classic milestones of development, these 
were attained more rapidly the higher the mercury level at 
12 months of age. I think they gave a very plausible 
explanation for this. That there was a confounder and that 
confounder was breast feeding. They showed the longer 
the breast feeding period is, the higher the mercury levels, 
and the well know literature that breast feeding is good for 
you. So this seemed to be a very reasonable confounder. 

Now I don’t see you are going to change that picture by 
any other kind of outcome. These kids were doing better. 

Well, they were doing better in terms of development 
milestones. 

Right, do you think they would do worse 
neurophysiologically? 

I don’t know. I have taken a lot of histories of kids who 
are in trouble at school. The history is that developmental 
milestones were normal or advanced and they can’t read at 
second grade, they can’t write at third grade, they can’t do 
math in the fourth grade and it has no relationship as far as 


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Dr. Rapin: 

Dr. Weil: 

Dr. Stein: 

Dr. Clarkson: 

Dr. Weil: 

Dr. Johnson: 
Dr. Sullivan: 


I can tell to the history we get of the developmental 
milestones. So I think this is a very crude thing as a 
measure of neurodevelopment. Hopefully we will be 
looking at much more sophisticated measures of 
neurodevelopment the next time we get into this kind of 
situation, but I think those of us who work with kids with 
neurodevelopmentai problems at school age would say that 
there appears to be very little relationship, except the 
severely mentally retarded and so on, between those kinds 
of things we are concerned about. Learning disabilities, 
reading disabilities, visual perceptual disabilities and 
developmental milestones. 

Most developmental milestones. Most developmental 
milestones, but not language. 

But most of the measurements that pediatricians make for 
developmental milestones are motive. 

But those are historical milestones you are getting from • 
parents of children who are school age, so you are deeding 
with memory at that time. That’s the problem. 

This by the way was the Faeroes. In the Seychelles we 
didn’t do that. Well, we did milestones. What we did is 
Fagen’s test and we did the Bailey’s, so the outcome 
measured in the Seychelles were different. 

And again, we could argue for hours about that, but I won’t 
do that. 

Kevin? 

I gave the value as one. I think the strength of the 
associations are mostly weak and the weaker the 
associations, the more likely bias might explain some of 
this. The issues on biologic plausibility, it seems about a 


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maybe. The dose seems to be small. There seems to be 
some issues of whether these small doses could cause these 
effects. 

Dose response. There does seem to be there may be 
somewhat of a dose response with some of the outcomes. 

One issue would be the quality of the data. Using 
observational data, computerized data sets. This is not 
designed as a study to look at the effects of these vaccines 
on these different outcomes, but it is using data collected 
for other reasons, so it is not a carefully controlled 
prospective cohort study to study. We are using data that is 
really collected for other purposes. That is not to say that 
the VSD, I think it has been extremely useful. You could 
probably look at some of these associations with a large 
sample size. I think it has been very useful for that. I think 
always in the back of our minds we have to remember that 
anything you can find in this has to be interpreted veiy 
cautiously because of the way the data are collected. 

One issue is the outcome. We have a lot of experts here in 
the area. That they are poorly defined. No consistent 
diagnostic criteria applied, and with probably a lot of 
misclassifications. Some who are called as having this 
diagnosis may not have had it. There were a lot of children 
who were not given this diagnosis, and maybe they did. I 
am not sure which way that misclassification works. 
Differential or non-differential according to the vaccines. I 
don’t know, but we know there is a lot of misclassification 
probably in the outcomes. 

Exposure to the vaccine. We really haven’t talked about 
that too much, although some information was given that 
there is a misclassification on vaccines. That some 
children whose record may say they have been vaccinated 
when in fact they have not received that vaccine, and some 


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of the no vaccine individuals may actually have received a 
vaccine. So we have a misclassification of exposure. 

Another issue is, is that a differential misclassification or a 
non-differential. It may be if the parents were getting the 
children immunized early are being more observant of the 
child’s development and growth, so that made me think 
that there may be a differential misclassification of the 
outcomes. 

Talking about some of the analyses. Well, there were a lot 
of statistical tests. I think we have to be concerned that by 
chance some of those might be due to chance alone. So we 
cannot always look at the P-value and say every one of 
those is true. If it is not statistically significant, it’s not 
true. I think there has to be a lot of caution in there. 

One thing that was not brought up was the assessment of 
effect modification. I always feel that if you are going to 
control for something, that you really should look to see 
whether there is an effect modifier of the relationship first, 
because you don’t want to control for something that 
modifies the vaccine or mercury levels in the outcomes. I 
never saw any information. Looking for effect 
modification might be interesting. There might be 
subgroups of individuals where maybe there might be some 
stronger association and no association in other subgroups. 

Again, part of the VSD, there is Lack of some of the 
variables that might be useful for assessing or that might 
modify this relationship or confound it. There was 
information given on birth weight, a very small sub- 
sample. FCS is not known very well. Ethnicity, breast 
feeding, so there is a lot of things that may be somehow 
involved in this that we really don’t have good, solid 
information on that. 


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As far as the mercury levels, again I think it has been 
brought up that we are talking about kids who are getting 
challenges with lots of different antigens, the more the 
mercury exposure is going hand in hand with the number of 
injections and other exposures, so in general I think it is a 
weak association from the evidence we have seen here. 
There are lots of problems here, but I feel we should 
probably go on and look at this a little more carefully. 

Dr. Johnson: Thank you. Paul. 

Dr. Stehr-Green: I also gave it a two. The evidence for causality is sparse 

because the determination of causality is based on many 
factors, not just statistical association and how strong that 
association might be, and many of my reasons have been 
stated already. 

I sort of went through and weighted for and against. 
Temporal association. I think there is evidence of temporal 
association in only the barest sense that I think occurred 
before diagnosis. However, there was nothing to show that 
the distribution of those outcomes, indeed they are real 
because I have a lot of questions about the consistency and 
voracity of those diagnoses. There was no analysis to show 
that the distribution of those over time is nothing different 
from the normal background breaks of occurrence. 

In terms of strength of association, even though I think 
there was evidence to form an association, I think at best 
they demonstrate a weak elevated risks for some of these 
outcomes. 

Consistency with other findings. There really are no other 
findings of similarly designed or similarly focused studies, 
at least of which I am aware of or at least that was 
presented, so we can’t really say that this is consistent with 
other findings. 


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Biological plausibility for the reasons that Dr. Koller 
stated, the levels of exposure in this study were likely 
lower than exposure levels seen in other studies where no 
effect was observed, so that kind of mitigates against 
biological plausibility. 

Although in effect it was an extrapolation from 
methylmercury to ethylmercury raised uncertainties. Even 
so, in the balance there is not tremendous evidence about 
plausibility. 

Dose response. That was really the hardest statistical 
analyses that was presented, and I think some of the 
analyses demonstrated dose response curves at some age 
levels. But again, the inclusion of these supposedly low 
exposure groups, the whole question of plausibility of 
ascertainment I think has to be weighed when considered 
against the relatively small significant dose response curves 
calls those into question. 

Finally, the issue of reproducibility, which is related to the 
issue of consistency with other findings. We never will be 
able to do human experiments per say, but there may be 
opportunities to do other types of studies as a dimensional 
rating that we will get into in the third question. We may 
be able to look at this more carefully to see if we can 
reproduce these, using operational data of course, to 
reproduce these effects. 

As an editorial note, I think asking us to assess causality 
was kind of a foregone conclusion. There is no way we are 
going to find that this was a causal relationship, based on 
the data and evidence presented. So I am not sure in that 
respect the results will be useful because I am not sure 
there was ever any possibility that we are going to find 
other ones. 


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Dr. Johnson: 


BREAK 
Dr. Chen: 


This is Dr. Walker’s comments. He gave it a two in favor 
of causality. Stronger results which validated data. As the 
data were validated, the results got stronger, at least in 
some cases. Relations do not depend on the extreme values 
of vaccination status. 

Against, uncertainty about the clinic. Confounding. 

Second, plausibility of medical, social artifacts and 
alternative explanations. 

Third, lack of supportive or event related toxicology, 
pharmacology. 

So he leans fora 1.8. 

Okay, we are a little bit behind on the previous schedule 
and we have tightened the schedule up by 30 minutes, so 
what Dr. Bernier has asked is that we take a shorter break 
than allocated. I think we trying to end at noon, is that 
right? I think we will try to leave at noon. My feeling is 
that the research can be shorter than this last round, is that 
the feeling? A lot has already been covered, plus it is 
written down. 


We felt that it was important to bring this data to wider 
scrutiny despite it being only phase I and despite as 
someone argued, that the data has shown very low relative 
risk. 

The main reason for that, I think we felt that unlike most 
other vaccine safety signals in the past which have come 
from VAERS and despite the problems of the events about 
the VSD, that in general the database was designed to look 
at safety issues and give them the precision on the exposure 


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Dr. Stein: 

Dr. Chen: 

Dr. DeStefano: 


side. We felt that it was really a hard quality of initial data 
source. That the dose response was probably some, but not 
all, selected biological plausible outcomes that may be 
associated with mercury, and that while we were concerned 
a bit about the multiple comparison issue, it is hard to 
explain away a dose response curve based on those 
multiple comparison arguments. And that whenever we 
tried to tier the data in terms of increasing the specificity of 
the diagnosis, in general we found either consistent or a 
higher relative risk. This was even when we tried to 
restrict it to more than one visit and when we did chart 
reviews and in general, in epidemiology that suggests that 
it is not a finding. 

We were very much considered about the utilization bias, 
as well as the lower level exposure groups, but that when 
we picked this non-biologically plausible outcomes, in 
general they came up with different curves. So that led us 
to kind of think those other biases should be consistent 
throughout, and we definitely felt that more definitive 
studies were needed with systematic review and Frank and 
Bob will present that. But that over 10 years of working 
with this database with probably over 25 studies over time, 
these very experienced Pis were worried that this 
information, given the current climate, do warrant a greater 
scrutiny other than us just plodding along, finishing our 
cases, et cetera. 

Again, I wanted to thank you all and give you all the basis. 

May I ask you, 25 other studies came from this database? 

Yes, and we have some review papers that we will supply 
you. 

I am going to start out with what is supposed to be the next 
step. What I am going to do is basically try to summarize 


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Dr. Chen: 

Dr. DeStefano: 

Dr. Rapin: 


and give you a little bit about what we have going on in the 
next step. Summarize what has been suggested by many of 
you as the next step, and then turn it over to Bob Davis, 
who as you recall when I talked yesterday, he said this was 
like a protocol. This was going to be at least a two phase 
study. The first phase was a screening and that is what we 
have been discussing, but the second phase was going to be 
the more definitive study. So I will turn it over to Bob and 
see what he proposed for a phase II study should be, now 
after the things we have discussed. 

First of all, the next step to a possible association. I think 
we mentioned this yesterday in terms of the consistency of 
findings via the replicator. We are trying to replicate these 
with data for another HMO. We have been in contact with 
Harvard Pilgrim Health Plan in Boston and they are trying 
to put together a data set similar to what we had in the VSD 
so we can try to replicate these analyses in other 
populations. They expect to have about 20,000 to 30,000 
children. This is on the order of the Group Health size 
cohort. They will try to use the same methods as VSD, 
although here we will have more a restricted A priority 
hypothesis if you will. Our intent is primarily to look at the 
speech billings and attention deficit problems. And we 
both have put it on here results by 21 . 

Is it possible for CPP? 

Yeah, a suggestion was made about CPP. I am not familiar 
with the data set. I have some questions about that, if you 
can fill us in. What ages were these children followed? 
Would they have been seen for these kinds of problems and 
the vaccination... 

Yes, through age seven. Talk to Karen Nelson and she will 
tell you all about it. 


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Dr. DeStefano: 


Vaccination data? 


Dr. Rapin: 

Dr. DeStefano: 
Dr. Brent: 


Dr. Rapin: 

Dr. Sinks: 

Dr. Orenstein: 

Dr. DeStefano: 

Dr. Clarkson: 

Dr. Gerber: 

Dr. Clarkson: 


But you have to talk with Karen Nelson because she has 
been minding this database for developmental problems for 
years. 

Vaccination data. 

The original collaborative perinatal project was to look at 
the cause for cerebral palsy. That is what it was originally 
designed for, and mental retardation. They accumulated 
everything. They registered the people at the time they 
became pregnant, so they had all the information collected 
on their prenatal care and they had many visits before their 
babies were even bom. 

The last visit was at age seven. 

Just to follow up on that, the National Institute for Child 
Health and Development, I don’t know if they have vaccine 
information. 

They do, they have published data on seizures. I know on 
whole cell DTP out of that database. 

There has been much discussion about a study of exposure. 
If you talk to Michael Gerber, you can see that there is one 
study in progress that NIH has been doing at the University 
of Rochester. There is no data available. 

The data samples of urine and blood from the infant and 
the hair samples from the mother... 

Do you want to just describe the study? What it is, Tom? 

Would you like to? 


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Dr. Gerber: 


Dr. DeStefano: 


Dr. Weil: 


Dr. Myers: 


Well, this is the attempt to look at the pharmacokinetics of 
ethylmercury in 40 infant/mother pairs. What we are 
attempting to do is get one group of infants who were not 
exposed to Thimerosal containing vaccines. It turns out 
fortuitously that the Bethesda Naval Hospital has not been 
using Thimerosal containing vaccines for the past two 
years. So we are going to use those infants, and a group of 
infants from Rochester, some of whom who were exposed 
to large amounts of Thimerosal and others who were 
exposed to a moderate amount. The idea is to look at these 
infants’ blood levels, urine and stool within one month of 
having received vaccination. Then at the same time look at 
maternal hair samples, as well as dietary histories from the 
mother to get some idea of potential baseline exposure in 
utero. Then get some sense of the pharmacokinetics of 
ethylmercury in these patients. 

There was a suggestion made earlier it is important in these 
pharmacokinetic studies that humans, if they would just 
adequately address this concern. And it was also suggested 
that we do more animal studies. One or more studies in 
animals. 

Just let me paraphrase that. That has to be 
neurodevelopmental toxicity studies. When you talk about 
animal studies, there are millions of kinds of animal 
studies, but there are now specific guidelines for 
neurodevelopmental toxicity and that is what you need to 
be looked at in this particular situation. 

And I would suggest that they ought to be ethyl versus 
methyl as well, to distinguish the relevant contribution. 
Another study which I think 

Dr. Clarkson is doing is looking at the contribution of 
ethylmercury and the types of vaccines that were given to 
children in the Seychelles. To look and see if we can 


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Dr. Clarkson: 


Dr. Braun: 


Dr. Stehr-Green: 


Dr. Brent: 


Dr. Gerber: 


Dr. Stehr-Green: 
Dr. Gerber: 

Dr. Rodewald: 


determine what the contribution of ethylmercury to their 
exposure was. 

I knew you would say that to get it in the record, but we are 
doing our best to find out about that. About the toxin 
exposure. 

Just to mention, and I think you may have seen the 
protocols, Frank, there are some collaborative studies 
planned between the Center for Biologies at FDA and 
NIEHS, looking at animals and the pharmacokinetics and 
also, if I remember correctly, histopathology in 
experimental animals dosed at various ranges of doses of 
ethyl and methylmercury. 

Frank, when you were describing the study of theNIH 
Bethesda study. University of Rochester, it seemed to be a 
very valuable, natural experiment source. If Bethesda has 
been giving vaccines without Thimerosal, is it possible to 
look at some of these same health outcomes? Da chart 
reviews? Or does the data exist in some way? That way 
you could separate out the other vaccine component effects 
from the Thimerosal effect. 

But they are only two years into the project. They 
wouldn’t have children old enough. 

Yes, according to last year’s. I don’t know how big that 
cohort is. It is the Bethesda Naval Hospital’s Pediatric 
Ambulatory Clinic. 

It may only be a future potential at best. 

It could, yes. 

I was wondering, when you are talking about the research 
agenda if it would be helpful to pose it in questions rather 


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than in types of studies and things like that. What is the 
hypothesis that you want to test. We have done it on a 
couple of those, but when it just says further animal 
studies, that is rather vague. 

Dr. DeStefano: These are just notes I have taken on the discussion. 

Dr. Rodewald: But I think that may be the helpful discussion and say what 

questions? Part of what sounds like it was discussed is the 
impact. We really tried to address causality directly and I 
wonder if that is something that is going to come up on a 
future slide in here. Because I am not sure how well you 
are going to be able to hit at some of the causality questions 
in here. I think to gradually try to hone in on that would 
help. 

Dr. Brent: With regard to sort of the administrative problems here, I 

can understand that with regard to the epidemiological 
studies, your group would be involved in orchestrating in a 
positive sense. Orchestrating the epidemiological data that 
is available in the United States. But with regard to the 
animal studies, who would be responsible? Would it be the 
FDA, because they have a wonderful facility in Arkansas 
with hundreds of thousands of animals and they could put 
together a valid project. Maybe you would want input 
from the group here to tell them exactly what you would 
like. 

And the pharmacokinetics, who would do that? Who 
would have that responsibility, because that is a small study 
to look at the mercury pharmacokinetics in a small 
population to get an idea of how long it lasts and what 
would happen after five doses? Would you have any 
different blood levels? In other words, we need some kind 
of administrative input in order to have all these things 
going on at the same time. 


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Dr. Weil: 


Dr. Johnson: 


Dr. Weil: 


And I say that because I wrote a last paragraph. It is sort of 
frightening to me, but I will read it. By the way, I have 
been involved in three lawsuits for the vaccine group, and 
they happen to be people who were given vaccines who 
were pregnant and the allegation was that the vaccine 
caused the birth defects. Let me Jell you, if you want to see 
junk science, look at those cases. It is amazing who you 
can find to come and testify that such and such is due to a 
measles vaccine. They are horrendous. But the fact is 
those scientists are out here in the United States. So let me 
read what I said. 

The medical/legal findings in this study, causal or not, are 
horrendous and therefore it is important that the suggested 
epidemiological, pharmacokinetic and animal studies be 
performs. If an allegation was made that a child’s 
neurobehavioral findings were caused by Thimerosal 
containing vaccines, you could readily find a junk scientist 
who would support the claim with “a reasonable degree of 
certainty”. But you will not find a scientist with any 
integrity who would say the refuse with the data that is 
available. And that is true. So we are in a bad position 
from the standpoint of defending any lawsuits if they were 
initiated and I am concerned. 

So it may not be the government doing some of these 
studies. If you could use any of the precedent from other 
drugs and other chemicals is smaller than the fact of 
dumping this back on the industry that uses the vaccines 
and ask the company to produce these studies. That has 
certainly been a pattern for an awful lot of things. 

Bill, when you say fund the studies, is that what you 
meant? 

Well, some of the companies will do them in-house 
because they have the expertise. Others may fund 


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Dr. Sinks: 


Dr. Brent: 
Dr. Sinks: 

Dr. Braun: 


somebody else to do them, depending on the amount of 
expertise. But the government has had a tendency, and I 
don’t know if they will in this case, but to rely on the 
industry to deal with the basics and then 
neurodevelopmental studies. With a little pressure, they 
may change their minds, but I don’t know that. 

Just to perhaps answer Dr. Brent’s question about the part 
of government that may be responsible, the National 
Toxicology Program at NTP is an inter-agency, collective 
if you will that is basically housed at NIEHS, and I believe 
Miles Braun probably was referring to a collaboration 
within NTP, which has FDA as part of the executive board, 
CDC, ASTDR, NCI, and I am pretty certain that George 
Lucere, who is about to retire at the end of the month, but 
was involved and I think that they are doing some initial 
bio assays, either in Arkansas now to look at ethylmercury. 
I think that is an appropriate route to be talking about. 

I agree, but what bio assays? 

I am not exactly certain what they decided to do. I think 
Miles probably described it. 

I looked at the protocols and I can’t really quote them to 
you. I think it is important to underline that these are 
planned studies and they depend on an argument, at this 
point as I understand it, between different agencies. NIH 
and FDA, but I think coming out of this meeting, if it is felt 
that is an important project to carry out, then that certainly 
could help it actually coming into place. Don’t get me 
wrong, these are not underway. They are planned. They 
have protocols. There is a lot of thought that has gone into 
these, but that is about as far, as I understand, where they 
are. 


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Dr. Brent: 


Dr. Braun: 


Dr. Chen: 


Dr. Myers: 


I think it is very important if that group, which is an 
excellent group, is planning to study, if they have some 
consultative help from some of the people who are here 
because we now have heard all the information here and 
have a wealth of information. They could provide the 
animal experimental people valuable information while 
planning the project. I would hate for them to go through 
with a $50,000, $100,000 or a $200,000 project and not 
have had information from this group which would help 
them design a better study. 

Well, anybody who would like to contact the people who 
are investigating, I will be glad to pass this on. If you want 
to give me a card or something, I will be glad to pass this 
on to those people who are planning on carrying this out. I 
would think they would want to get the kind of consultation 
you are talking about. 

Maybe arrange for some senior body with the protocols to 
be sent to the consultants for review. Be it us or be it 
Tom’s group or whatever. 

I think in answer to you question, although the meeting has 
been convened and is being led by CDC, if you look 
around the room, we are all here from each of the different 
agencies, and the reason for that is we are looking for the 
input for cross agencies, not just for CDC. 

Can I go back to the core issue about the research? My 
own concern, and a couple of you said it, there is an 
association between vaccines and outcome that worries 
both parents and pediatricians. We don’t really know what 
that outcome is, but it is one that worries us and there is an 
association with vaccines. We keep jumping back to 
Thimerosal, but a number of us are concerned that 
Thimerosal may be less likely than some of the other 
potential associations that have been made. 


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Dr. Gerber: 


Dr. DeStefano: 
Dr. Gerber: 

Dr. DeStefano: 
Dr. Gerber: 

Dr. DeStefano: 


Some of the other potential associations are number of 
injections, number of antigens, other additives. We 
mentioned aluminum and I mentioned yesterday aluminum 
and mercury. Antipyretics and analgesics are better 
utilized when vaccines are given. And then everybody has 
mentioned all of the ones we can’t think about in this quick 
time period that are a part of this association, and yet all the 
questions I hear we are asking have to do with Thimerosal. 
My concern is we need to ask the questions about the other 
potential associations, because we are going to the 
Thimerosal-free vaccine. If many of us don’t think that is 
a plausible association because of the levels and so on, then 
we are missing looking for the association that may be the 
important one. 

I thought I would put that out. That we shouldn’t just think 
in terms of mercury. 

Just to follow up on Marty’s comment, it seems to me that 
during the time that this study was done, 1992 to 1997, at 
least at Northern California Kaiser, there was a substantial 
number of children involved in vaccine trials. The 
vaccines that those children would have received would not 
have shown up in the CPT coding. When you go back and 
reanalyze the data, I wonder if there would be some way 
you could determine what other vaccines these children 
may have received as part of the clinical trials? 

We know if they received any experimental drugs. 

You would know? 

Yes, we have the data. 

Okay, but you did not include that in the analysis? 

Tom, did you look at those? 


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Dr, Sinks: 


I haven’t separated them up. 


Dr, Rhodes: 

Dr. Gerber: 
Dr, Chen: 


Dr. Myers: 


Dr. Johnson: 

Dr, White: 


As far as we knew none contained Thimerosal, so they 
were not included. 

Right, but it may not be Thimerosal. 

To address Marty, I think that is quite reasonable, although 
we have a limited amount of manpower because of what 
we just studied. At the moment, I would think most people 
around the room would argue these are biologically 
plausible outcomes potentially related to mercury, and then 
we will keep the other ones in mind. But hopefully we 
could do some of these studies to kind of rule it out, and yet 
if the association still stands, then we can look at some of 
these other hypotheses. That is the first step. Given the 
amount of time today, maybe just focus on mercury. 

I agree with you, Bob, but the think the conclusion is there 
is an association between vaccines and the outcomes that 
we cannot reject and of which one compliment of the 
vaccines that is associated is Thimerosal, but it is only one 
of the associations. I don’t think it is any more plausible 
than some of the others. And I think I heard several of the 
consultants say the same thing. 

That is an important prospectus, but our charge today is to 
focus and pick out obviously the mercury and focus in on 
that. That is a pretty tall order. 

I thought we were looking at future studies and howto 
delineate what is causing this. If they gave it a one and a 
two, they thought it was a causality in this and there is 
aluminum. You could run these tests in another arm, in an 
animal study, a lot cheaper than restarting it up again. I 
think it is a good suggestion and the industry 
representatives that provide bulk for these vaccines. I’m 


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Dr. Caserta: 


Dr. Stehr-Green: 

Dr. Brent: 


Dr. Orenstein: 
Dr. Brent: 

Dr. Orenstein: 


glad you invited us here because I think they would be 
willing to work and provide that. It would be cheaper to 
add that arm. 

One of the things I learned at the Aluminum Conference in 
Puerto Rico that was tied into the metal lines in biology 
and medicine that I never really understood before, is the 
interactive effect of different ions and different metals 
when they are together in the same organism. It is not the 
same as when they are alone, and I think it would be 
foolish for us not to include aluminum as part of our 
thinking with this. 

I think generically, you know there are books on mercury 
and Thimerosal. Because of these other concerns, I think it 
will be important when we design all of these studies to 
think about ways of excluding other possible genealogic 
agents, either in the design or in some way so they can do 
the analysis that way. 

The advantage of the perinatal project is some of the 
vaccines that would be included today were not available 
then. The only thing we had as far as I recall is the 
Diphtheria, Tetanus and Whooping Cough. You didn’t 
have the Hepatitis. You didn’t have some of the other 
vaccines, so that is a unique group of people that could sort 
of sort out some of the other issues that have been raised. 

How about smallpox? 

They had smallpox. 

You have to add smallpox and IPV. In fact, one of the 
studies from the perinatal project suggested an increased 
risk of tumors in the off spring of parents who received 
three CBL. Heard of these associations. 


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Dr. Sullivan: 


Dr. DeStefano: 
Dr. White: 

Dr. Myers: 

Dr. Verstraeten: 
Dr. Rhodes: 


Are there any clinical trials begun in the last 12 years 
where it will be enough variation, like in HIB trials for 
example, where Thimerosal containing vaccine was given 
to some and not to others in a population that might 
hopefully define information about developmental 
disorders later on? For example, the HIB study that was 
done at Kaiser. I don’t know how varied the Thimerosal 
exposure would be in those kinds of studies. 

In Northern California we tried. 

There was a huge study that was done for pneumococcal 
conjugates and as a control they used the meningococcal 
disease and I don’t know, it’s either neither contained 
Thimerosal. Well, there you go. 

That’s right, and also we would have to wait some time 
before, but that original HIB efficacy trial, California used 
the single dose vaccine that did not contain Thimerosal. 

It is still interesting because it contained some of the other. 

One thing that hasn’t really come up is there are plenty of 
other kids, even just at NCK and Group Health, who 
haven’t taken part in the current analyses. In other words, 
if you look at current eight, nine and ten years old, and if 
you had some information about what their Thimerosal 
containing vaccines might have been when they were 
infants, and if there would be enough variation in those 
kids and it’s a controlled setting, you are looking at 
outcomes maybe you would feel more secure about than 
the seven, eight, nine, ten. There are kids at those ages 
now, but the question would be how good would the 
vaccination information be on Thimerosal going back to 
that same time. If you had that information you wouldn’t 
have to wait three of four years. 


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Dr. Weil: 


Dr. Orenstein: 

Dr. Rhodes: 

Dr. Rapin: 

Dr. Chen: 

Dr. DeStefano: 

Dr. Davis: 

Dr. DeStefano: 
Dr. Davis: 


Most of those kids are in schools that require a vaccination 
record, which includes not only the date and the vaccine, 
but the lot number, so if you look at eight and nine year 
olds now, you will find probably out of most school 
systems, some pretty good immunization records. My 
guess is it would not be hard to find a sample. 

Not only that, but the validity of some of those school 
records has been problematic in terms of people getting 
extra vaccinations because they had vaccinations that did 
not get reported into the school records. Minnesota turned 
out to be very accurate. Dallas County turned out to have a 
substantial inaccuracy of data. 

I’m talking about kids who are in the HMO that have 
reflected of this other data. 

How about the Mayo data? 

Why don’t we let Bob and Frank present exactly the sense 
this cohort has the best information and exposure going 
back to about 1990 , and so be able to kind of quickly 
finish. 

I think one last thing was going to go like, what would you 
do in that kind of follow up study? I think the same issues 
would come up during Bob’s presentation. He is going to 
present us a more specific proposal rather than general 
issues. 

Probably not as specific as you had hoped. 

Not as general as I have. 

As we have all talked about, current studies lack a lot of 
data, including mercury intake of the mothers during 
pregnancy. I am talking about the current studies that we 


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are looking at today and yesterday. And also a lack of 
information on breast feeding as 

Dr. Clarkson pointed out, which is breast feeding is a 
mercury exposure vehicle and also a way to improve 
neurologic functioning. 

It is not obvious how this might affect the current study. It 
simply is not obvious to me, could it be related to both the 
outcome which is very plausible, but could it also be 
related to Thimerosal exposure at one to three months? 
That is tenuous, but I am still not convinced. I think Phil 
made the strongest argument that there might be some 
confounding that has actually entered into our data. We 
thought this was actually a wonderful, natural experiment 
when we started out. Phil pointed out the fact that it is a 
natural experiment, however, it may not be wonderful. 

Next slide. Just to point out very quickly that these current 
studies also lack the usual suspects, which are alcohol, 
smoking, nutrition prenatally, lead exposure and nutrition 
postnatally, demographics including race and ethnicity and 
socioeconomic status. 

Again, while it is clear that these are related outcomes, 
neurodevelopmental and neuropsychiatric developments at 
five, six and seven years of age, it is not clear how these 
are related to Thimerosal exposure going to three months 
of age. 

Next slide. This is the thing we are all worried about. Due 
to time, I am not going to go into it again. It’s a signal that 
has remained after taking birth weight into account, 
although crudely, after we have limited it to kids who have 
had at least two visits for the outcomes of interest. When 
we limited it to second diagnoses, and then when we 
excluded children with competing cognizant diseases of 
interest, this signal has remained. 


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Next slide. I am going to be a little controversial here, in 
that I think there is a possibility to conclude the analysis 
without going a lot further. Phil pointed out something that 
he went through very quickly and then I spent a lot of time 
thinking about that last night, which is that in our zero 
exposure group we have a lot of kids that were just about to 
be vaccinated, so we may have been too conservative in 
how we considered our zero vaccination groups. So I think 
we should play around a little bit with widening our 
vaccine exposure window. I am not talking about the 
dosage, I am talking about our one month time period. I 
think a small group of us should sit down and think about 
perhaps at one month and play around with the definition 
there. That is worth revisiting because I was worried, as 
Phil pointed out, that our one month window excluded kids 
who were literally one or two days away from being 
vaccinated. There was other data that I won’t get into now 
that actually suggested that in fact may have played an 
important role. 

Also this business of our stratification by time. I think we 
have beaten this one into the ground. I think we may have 
dropped a lot of risk sets if we stratified by time by one 
month. I think we should go back and reconsider using 
two, three or four month time windows. I am not a big fan 
of secular trends occurring that fast within this time 
window. I think we should look at that. 

I think we could perhaps include some previously excluded 
children, but this is something that Miles Braun, I, and 
some other people were talking about, which was using 
another controlled outcome. Not gastroenteritis, not 
conjunctivitis. I think we should another control outcome 
group, chronic abdominal pain, which I think all the 
pediatricians and parents in the group would realize that 
parents who are likely to bring their children in early for 
vaccination would also be more likely to bring this in for 


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medical attention, and then see if the same signal persists 
with recurrent abdominal pain. 

Not so much for this one because the signal disappears with 
these three reanalyses. I actually think we should stop. 
I'm not on your committees. I’ll say that being very 
controversially. I will say that I have noticed, myself and a 
lot of very bright people have told Tom that they have 
found the problem in his analysis and they have made 
suggestions very similar to mine, and he has always called 
or emailed me the next day and said I reanalyzed it and the 
signal was stronger. So I think these are good suggestions. 
I do not think it is going to matter. 

Next slide. If the analyses remains positive, I don’t think it 
is ever going to be possible to differentiate increased health 
care seeking behavior among families whose children are 
vaccinated on time. I don’t think we have the capability of 
doing this, and suspect that the same finding will be 
replicated in the Harvard Pilgrim. If the people at Harvard 
Pilgrim can do it by June 21 st , I will be amazed. 

I am more worried that Harvard Pilgrim won’t have the 
power. That the signal will fluctuate up and down so 
much, that we really won’t know what to do with the 
results from Harvard Pilgrim. I think that’s an egg in a 
basket and I don’t think we should wait for it. 

Next slide, please. So this is what I am proposing. I 
actually think we should do a cohort study using the 
population that perhaps we have already. We have got to 
define the population to study based on their known 
vaccine history, so their known exposure. You don’t have 
their blood levels at the time of vaccines, but we know their 
vaccine history and we could do it. We could select 
Northern California Kaiser, Group Health Cooperative, 
Northwest Kaiser and Southern California Kaiser, and then 


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we could measure their outcome using a carefully 
measured set of neurodevelopmental and neuropsychiatric 
tests at one or more ages. This is not nearly as easy as it 
sounds because what we are really concerned about is 
exposure at one month and three months. But we may also 
want to know how about at one month and not at three 
months? How about two months and not one month. It 
would be difficult to do that while preserving enough 
power to see an effect at all the different vaccines levels. 
Because I am talking about bringing in children who had 
zero Thimerosal levels, 37.5, 50, 67, 75 and maybe truncate 
it there. So we have five exposure levels and then we have 
a two by two design. So what we would need to think 
about is actually, this is probably 500,000 children. I think 
we could find enough. It actually becomes a matter of 
findings rather than our ability to find the children. 

Let me go one more slide. I want to talk about why am I 
proposing this? I think this actually breaks the link that 
probably exists in the observational study. 

Children in this proposed cohort could have seen health 
care providers many times or actually never. We don’t 
care about them. Our analysis of the neuropsychiatric and 
neurodevelopmental outcomes is no longer dependent on 
the parent bringing them in. We are going to insist that 
they come on in. Hopefully we will have good 
participation, and we are going to study them at six years of 
age, regardless of whether they never saw a doctor at all. 
So we are actually breaking that link. So we going to give 
each child now an equal chance of having the outcome, 
aside from their Thimerosal exposure which is what we are 
studying in order to find their populations to study different 
exposures. 

There may still be some confounding because people who 
went to some clinics may have gotten very little 


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Thimerosal levels based on certain characteristics of the 
clinic they attended, and that may be an observable 
confounding variable. 

Other confounding information could be potentially 
collected at the time of , examination, including 
socioeconomic status, pregnancy exposures, smoking, 
alcohol. Some underreporting and under ascertainment of 
confounding will certainly exist and will certainly be 
diluted over time, but I have no reason to suspect that there 
will be a differential dilution or under-ascertainment by the 
Thimerosal sets. 

To answer Walt’s long standing question, I doubt this will 
allow us to differentiate Thimerosal. A lot of people have 
the same question. I don’t think this will allow us to 
differentiate antigen number or vaccine number from 
Thimerosal, but it will get us a lot further down the road. 

We could draw blood, and actually I would encourage 
people to think about drawing blood to look for gene 
environment interaction studies, because there may be a set 
of children in here that are particularly prone to Thimerosal 
related outcomes. 

Let’s talk about the confounding that is slightly true. The 
early receipt of vaccine in this study, children who have 
high levels of Thimerosal now at one month and three 
months of age are likely to belong to parents who are 
different, but why are they different? They are different 
because they are much more attentive. They are much 
more on the ball. I am really struggling here to use the 
term that is politically correct. The only term I can think of 
is the smarter parents, So actually what is this going to do? 
This is actually the confounding that might exist, although I 
don’t know. The confounding that will exist will be a 
negative confounding. This is the children with the high 


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Dr. Verstraeten: 


Dr. Rhodes: 


Dr. DeStefano: 


Dr. Rhodes: 


Dr. DeStefano: 


Thimerosal at the late ages, who are likely to be perhaps 
from better parents. The neurodevelopmental and 
neuropsychiatric outcomes are likely to be better. 

I am not suggesting that as a reason to do the study. I am 
just pointing out that if you think about where the 
confounding is going to be focused in this particular study, 
the one that I would be worried about, the validity of the 
study, is that it will have several outcomes. That early high 
Thimerosal exposure will be associated with 
neuropsychiatric scores. 

One thing you can easily add, one arm would be to 
compare DTP-HIB combined versus separate, and I think 
with very small numbers you will have enough power, 
doing this kind of testing, to identify the difference 
between mercury, Thimerosal and the other, because they 
have the same antigens, the same amount of aluminum and 
probably a lot of the other stuff that is in the vaccines. 

As you are thinking here, I think it would be important to 
sample from both cases and not cases though where you 
think a trend stands. I would argue for taking samples of 
cases and not cases. 

Well, this is a cohort study, so it kind of separated it 
completely from cases. 

But I think it would be important to say in those who have 
been called cases, do your tests pick up anything? 

Let me argue from just strictly a pragmatic point of view. 
We actually are not looking for cases, we are looking for 
minute differences in neuropsychiatric and 
neurodevelopmental outcomes. I think dichotomizing 
people into cases, while serves a very sophisticated sample 


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Dr. Rodewald: 


Dr. Davis: 


Dr. Rodewald: 


Dr. Davis: 

Dr. Rodewald: 


Dr. Oakes: 


Dr. Davis: 
Dr. Oakes: 


scheme, may actually not be what to do for this particular 
one. 

I would be more cautious about your ability to pick which 
direction the confounding is going to go. I think, for 
example, the birth dose of HepB is not usually quite 
processed the same in the U.S. and that is something that 
might be the other way. Trying to weight the 
preponderances, in assuming clinic policies in terms of 
how early they get kids in and perhaps physicians are more 
worried about certain parents getting them in there early for 
this. I just think it is really difficult. 

Yes, you said earlier the adoptees of the HepB. HepB is 
what you are saying. 

And even getting kids in early after they are bom. I mean a 
lot of pediatricians get the kids they are most worried about 
in earlier. That would go the opposite. 

Right. 

And the pediatricians opinions and practices dominate over 
parents preferences in terms of vaccinations and we know 
that from studies. 

I am going to have to leave momentarily, but first, is it 
feasible to contact people in these cohorts and select 
samples based on either exposure outcome history and go 
back to them? 

I’ve not done it, but everybody is saying yes. 

It is ethically and practical to do this? So I would certainly 
argue to doing some kind of master case control study. In 
that case maybe on small groups. I was wondering if you 


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Dr. Davis: 


Dr. Oakes: 


Dr. DeStefano: 


Dr. Oakes: 
Dr. Weil: 


Dr. Davis: 


would have any idea on the numbers you would need in 
your cohort study? 

No. I really don’t think you can get enough to do a control 
study. I really think we have to move away from the idea 
that we can actually ascertain cases here. I think what we 
are really looking for is an age of... 

The master case control would have a different purpose. It 
would be to try to get at the ascertainment bias and other 
confounders. I’m sorry, I was putting two things together. 
That is a different issue, but I think if it is feasible to do 
that, even on a fairly small number of subjects and we want 
to do that, the people would have the hardest possible 
outcomes and some carefully thought out matched sample 
of controls. 

The problem you would have is like the cases, we are still 
going to have to identify not only the cases we know about, 
plus we screen. You’d still have this problem of 
ascertainment that have been identified as cases now in our 
database. 

Well, you would know if they were really cases after... 

Let me just before you get too far. You are going to run 
into some big ethical problems if you try and identify 
people from this study for some characteristic. If you look 
randomly into the study, the ethical problem won’t be 
great, but the Human News Committee, if it is any good at 
all, is going to give you a very hard time if you try and 
identify people who are by number code only because they 
have a finding. That will violate all the rights to privacy. 

In other words we are actually identifying them based on 
the vaccine. 


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Dr. Oakes: 


So it wouldn’t be feasible to do it based on an outcome? 


Dr. DeStefano: 

Dr. Davis: 

Dr. Johnson: 

Dr. Oakes: 

Dr. Rhodes: 

Dr. Davis: 

Dr. Rhodes: 

Dr. Davis: 

Dr. Verstraeten: 

Dr. Davis: 


It wouldn’t be, but I think we are still concerned about the 
ascertainment bias still being... 

Recreate this, the bias that I am trying to get rid of. 

But isn’t that what you are focusing on here? You are 
going to take what is called the positive endpoint and you 
are going to see if it is real. 

Because you would sample the controls randomly and get 
them from those eligible. There be no ascertainment bias. 

I think the think is forget the automated outcome data. Go 
to the cohort and start with the exposure groups and the 
outcome can be defined upon the results of the tests. 

So we are going to hire people to do careful 
neuropsychiatric and neurodevelopmental? 

And I think at the same time you could, as an arm of this 
study, have people who are called cases in the automated 
data, people who are not called cases, and see whether 
these tests have any difference in those groups or not. 
Now, that would be interesting to know. 

And you would be checking your analyses, but I would 
strongly urge you not to guess. 

To answer your question on sample sizes, depending on the 
type of tests and the difference you want to detect, sample 
sizes range from like 300 to 1,000 only. I think that is 
pretty close to the sample size in the Seychelles. 

Is that for the entire study? 


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Dr. Verstraeten: 


The entire study. 


Dr. Sinks: 


Dr. Cordero: 


Dr. Stehr-Green: 


A couple of comments. I think your proposal goes very 
well in line with the beautiful studies that have been done, 
both in the Seychelles and the Faeroes, which are exactly 
this type of thing where you start from the exposure. You 
are not trying to determine case, you are trying to 
determine some difference in the neuropsychiatric tests, 
which has exquisite sensitivity and probably much greater 
power than trying to deal with cases. If you are going to do 
a master case control study, what I would recommend if 
you are going to look at cases is that you put a lot of 
emphasis into some standardized battery for determining 
who is and who is not a case, because I think that is one of 
the limitations with the data set that we have. 

And I think the main purpose to do that particular study is 
to be more confirmatory of testing what Tom has presented 
to us. I think you are testing a very different thing which 
is, is this similar to methylmercury exposure? What we 
believe is there in terms of that biological plausibility. 

I would like to sort of follow up on what Tom said. It 
seems the question we really are asking here is does 
mercury or Thimerosal in vaccines pose a risk for selected 
neurobehavioral problems and therefore, I think having an 
accurate measure of ethyimercury is essential in whatever 
study. And just answering the question of having exposure 
by vaccines may be sufficient, the question still remains. Is 
it mercury or is it something else? So I think we are 
talking about measuring mercury and perhaps measuring 
other vaccine tendency exposures, then having some 
systematic way for looking at the outcomes and being able 
to classify appropriately what happened. 

I just want to add one endorsement for a master case 
control study. In a case control study you have more 


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Dr. Davis: 


Dr. Johnson: 


Dr. Clements: 


freedom to look at other exposures, more than just the 
Thimerosal in the vaccines. So it might be useful to do 
that. It might give you an opportunity to look at other 
plausible etiologies. 

We are going to bring the kids ih. Once you do that, your 
study cost has been incurred. If you do a one hour 
interview of diet and maintenance. So I have to do that 
anyway. Not to see the entire cohort. 

I think this has been an exceptionally useful and strong 
discussion, and because people have to leave we are going 
to have to cut it at this time. If we have time to come back 
before noon, we will. 

I think there was a lot of recognition, and certainly I 
believe in most peoples minds, the implications of dealing 
with the composition of vaccines for the international 
community, and John Clements would like to make some 
comments at this time, then we will have Paul give Us his 
rapporteur’s comments. Then depending on the time we 
will come back to the discussion of research approaches. 

Thank you, Mr. Chairman, I will stand so you can see me. 

First of all I want to thank the organizers for allowing me 
to sit quietly at the back. It has been a great privilege to 
listen to the debate and to hear everybody work through 
with enormous detail, and I want to congratulate, as others 
have done, the work that has been done by the team. 

Then comes the but I am really concerned that we have 
taken off like a boat going down one arm of the mangrove 
swamp at high speed, when in fact there was no enough 
discussion really early on about which way the boat should 
go at all. And I really want to risk offending everyone in 
the room by saying that perhaps this study should not have 


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been done at all, because the outcome of it could have, to 
some extent, been predicted and we have all reached this 
point now where we are left hanging, even though I hear 
the majority of the consultants say to the Board that they 
are not convinced there is a causality direct link between 
Thimerosal and various neurological outcomes. 

I know how we handle it from here is extremely 
problematic. The ACIP is going to depend on comments 
from this group in order to move forward into policy, and I 
have been advised that whatever I say should not move into 
the policy area because that is not the point of this meeting. 
But nonetheless, we know from many experiences in 
history that the pure scientist has done research because of 
pure science. But that pure science has resulted in splitting 
the atom or some other process which is completely 
beyond the power of the scientists who did the research to 
control it. And what we have here is people who have, for 
every best reason in the world, pursued a direction of 
research. But there is now the point at which' the research 
results have to be handled, and even if this committee 
decides that there is no association and that information 
gets out, the work has been done and through freedom of 
information that will be taken by others and will be used in 
other ways beyond the control of this group. And I am 
very concerned about that as I suspect it is already too late 
to do anything regardless of any professional body and 
what they say. 

My mandate as I sit here in this group is to make sure at the 
end of the day that 100,000,000 are immunized with DTP, 
Hepatitis B and if possible Hib, this year, next year and for 
many years to come, and that will have to be with 
Thimerosal containing vaccines unless a miracle occurs 
and an alternative is found quickly and is tried and found to 
be safe. 


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So I leave you with the challenge that I am very concerned 
that this has gotten this far, and that having got this far, 
how you present in a concerted voice the information to the 
ACIP in a way they will be able to handle it and not get 
exposed to the traps which are out there in public relations. 
My message would be that any other study, and I like the 
study that has just been described here very much. I think 
it makes a lot of sense, but it has to be thought through. 
What are the potential outcomes and how will you handle 
it? How will it be presented to a public and a media that is 
hungry for selecting the information they want to use for 
whatever means they have in store for them? 

I thank you for that moment to speak, Mr. Chairman, and I 
am sorry if I have offended you. I have the deepest respect 
for the work that has been done and the deepest respect for 
the analysis that has been done, but I wonder how on earth 
you are going to handle it from here. 

Dr. Brent: Mr. Chairman, I think that was eloquent statement. The 

question that I have with regard to perceiving this data with 
some type of reanalysis, is that because of the diverse use 
on vaccination, no matter what you come up with 
somebody on one side will accuse you of doing something 
to get a negative result. Then if you come up with a 
positive result using the same data, the person on the other 
side will say see, we were right, it is causal. So I really 
encourage the investigators to get other populations to 
study because of the fact that I do not think reanalysis of 
this data is going to be as helpful as we would hope. It 
would be helpful if it wasn’t in this room, because we 
know of the integrity of the scientists and we know they are 
pursuing it for the truth, but other people out there don’t 
have those feelings about anybody who is involved in these 
studies. That is my concern and that is why I think Dr. 
Clements comments are so to the point. 


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Dr. Johnson: 


Dr. Stehr-Green: 

Dr. Bernier: 


Dr. Brent: 
Dr. Bernier: 


This focus on new research that has been mentioned that 
Dr. Clements’ comments raised is the need, and this applies 
to the vaccine manufacturers to develop another, an 
alternative preservative anti-microbial measure for use in 
childhood vaccines than ethylmercury. It is possible in 
single dose. There is a lot of wonderful advances in 
manufacturing biologicals and it should be applied here I 
think. 

Paul Stehr-Green, do you want to give us your rapporteur’s 
summary of everything? 

Let me say, my understanding is that whatever I say will be 
expanded upon once I have the benefit of seeing all the 
speakers notes and that a written summary will be 
submitted to at least all the consultants, is that correct? 

Yes, we haven’t asked to do that yet, but Paul will be 
writing a report. We have a very short turn around for this. 
We want to get the report prior to the ACIP meeting,- so we 
are looking at about a week to get this report. We want to 
get as much feedback from the eleven as possible, so if you 
could please collaborate with us in trying to get a quick 
turn around on that. So we would not want to get the report 
if you did not think it was a fair assessment, so Paul is 
going to have something as soon as possible. 

But you want these written? 

Yes, I want you all to turn in your sheets, please. And also 
I would like to invite anyone else who has been in the 
meeting and heard this, I think the sheets have been widely 
available and anyone who has filled them out, please, we 
would love to collect those as well, even though we are 
focused on the eleven that were officially hired to be CDC 
consultants. 


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Dr. Brent: 


Who do I inform that my e-mail address is wrong? 


Dr. Bernier: 

Dr. Stehr-Green: 


You can tell me. 

Anyway, my point of reasoning was if you feel I have 
given an inappropriate slant or misrepresented comments 
you’ve made or others have made, we will have at least two 
opportunities to correct that, in this discussion and then 
when the written report comes out. 

For the sake of time, when I write a written report my 
intent will be to summarize the sort of historical events that 
led up to this meeting. Both what has happened over the 
last several years and more specifically, Dr. Myers 
summary of the workshop last August at NIH. 

Of note, I think it was important, or at least I glean from 
Dr. Myers’ presentation, that in fact the group last year 
made a similar recommendation to what John Clements just 
said, and that is you may not want to do this study because 
the results are not likely to be useful for resolving this issue 
and in fact may raise concerns and havoc in locations with 
which we cannot deal based on this study. Is that a correct 
interpretation? So I think it is important that he verifies 
what John said, and he provides the setting for when this 
study was embarked upon. 

I also intent to summarize sort of the generic aspects of the 
Vaccine Datalink at CDC and how the operation is set up. 
But of course most of the emphasis will be on the Phase I 
study. What I hope to do is demonstrate that through 
exhaustive analyses and very careful attempts to tease out a 
variety of problems with confounding possibilities, with 
other possible exposures, with other plausibles that we 
don’t understand, with perhaps uncertain and inconsistent 
diagnoses and with this, to my mind, the looming issue of 


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the potential differential utilization of health care and the 
ascertainment bias that might carry with that. 






Despite all those things, Tom and colleagues were able to 
demonstrate that there was a signal present, and I think the 
group verified that indeed there was a signal. However, 
that signal was not strong enough either by itself and in the 
context of others such as biological plausibility and so 
forth, it was not strong enough to support an inference of 
causal relationship. In fact it was a signal that deserved 
further investigation and that raised some perhaps 
disquieting possibilities. 

I think in many respects the group of consultants has made 
my job a lot easier in that there was very little controversy 
in the conclusions. As a whole, the group was pretty 
unanimous, in fact we were unanimous, in saying that 
additional research is needed. However, that the current 
results were weak for a variety of reasons. Again, the 
inconsistent and uncertain diagnoses, the looming 
possibility of ascertainment bias and uncertainties as to 
whether or not we can separate out Thimerosal effects with 
other vaccine components, or even other exposures that 
may be somehow statistically correlated with vaccine 
administration. 

Nonetheless, there was a consistent opinion that these weak 
findings should be followed up, and in fact in this last 
discussion we talked about different research avenues that 
might be pursued to get a better handle on this association, 
this signal if you will. 

Again, with regards to the question of whether or not these 
results support causality, as I said before I think the group 
was unanimous, except for possibly Dr. Weil, in suggesting 
that there was not anything close to sufficient evidence to 
support a finding of a causal relationship. And again, we 


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went back to these issues of uncertainty about the 

diagnoses, uncertainty about the possible biases and 
confounding that could not be accounted for in the analysis 
because we did not have the data. 

There were also concerns brought forward from previous 
human studies and animal studies that suggests the 

biological plausibility of this association may not be strong 
and supportive in that the calculated exposures in this 
setting with which this study dealt were actually below, in 
some cases quite far below, no effect levels that had been 
seen in human and animal studies previously, with a 
presumably more toxic form of organic mercury. So the 
fact that we were extrapolating from methylmercury to 
ethylmercury, the fact that we were extrapolating down a 
curve into an area where there had not been any 

observations of any effects, and yet still suggesting that 
there was this statistical association, .it was my 

interpretation and it seemed the interpretation of others, is 
that the evidence for biological plausibility of this 
association was not very strong. 

So in fact in summary, f think the mean list of the group 
was 1.8 in the rating scale. So as a group we said there is 
no evidence for causality for again the same reasons and 
recurring theme that came up. 

In terms of the next steps, I don’t have the same feeling of 
"*** unanimity. I think this is a work in progress. I am not sure 

how we are going to resolve that, but we had some very 
good ideas put forward. The cohort study that Bob 
described seemed to have a resonance not only among the 
members of the panel, but also the wider audience here 
today. I think though that based on Bob’s discussion and 
some of the comments that were made, there are a lot of 
issues that have to be resolved. How do we define 
exposure? How do we define diagnosis or the outcome 


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Dr. Weil: 


Dr. Brent: 

Dr. Stehr-Green: 


measures? How do we choose the subjects? How do we 
try and collect the information and or if we can collect the 
information, control some of these biases that have 
troubled these discussions over the past two days? 

I guess my sense of it was that in terms of what the next 
steps are, we got some good ideas that have been put out, 
but they are pretty rough at this point and need further 
refining, and maybe that is not the role of this committee or 
these consultants, but I get the feeling as though we have 
resolved many of those issues. We had some promising 
avenues and the doors were starting to come open, but we 
have to peek through those doors or maybe walk through 
those doors and begin to feel some issue that are going to 
arise. 

So I will leave it at that and see if people feel I hit the nail 
on the head or missed the mark or would like to add or 
subtract. 

Toward the end you made the statement of the 1.8 level or 
whatever it was indicated no causal relationship. I don’t 
think that is quite true. I think it indicates there is no 
agreement that there is a significant sense of a relationship, 
but it might say the people felt there was some relationship 
somewhere. There is something in these data that relate the 
number of micrograms of mercury, or at least the group 
that is represented, that group seems to be related to 
something called speech delay. The data was significant. 

Causal. 

What I hope to do is draw distinction between findings of 
association and findings of causality, and I think the group, 
based on the answer to the first question, said that there 
was a finding of association. Perhaps weak, but there was a 
finding of association that needed to be pursued, but then 


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Dr. Weil: 

Dr. Stehr-Green: 

Dr. Rapin: 

Dr. Weil: 

Dr. Rapin: 

Dr. Weil: 

Dr. Rapin: 

Dr. Johnson: 

Dr. Chen: 


when you considered the issue of causality, the group was 
not willing to say there was sufficiently strong evidence to 
support a finding of causality. 

Or refute. 

Or refute, right. When I restate it that way, is that a fairer, 
truer characterization? 

There is also the question of relevance. I mean is this tiny 
change relevant clinically? This business, you raised the 
IQ point by one point over a large population, it is 
statistically significant, but is it relevant? Can we measure 
the IQ that accurately, that this one little point is relevant? 
I think that is another matter altogether. 

Now they are reducing lead from 10 to 5, that is exactly the 
argument that is being used. That reducing acceptable lead 
levels from 10 to 5. The point is that is being discussed as 
a real possibility and it is based on a very tiny increment. 

I think the whole lead issue to be revisited. 

But there is in other words another toxic compound that 
need to be looked at for some of these same reasons. 

Even in my grandchildren, one IQ point I am going to fight 
about. 

Paul, the hardest job anyone has at a conference is to be the 
rapporteur, and I am impressed. You are on top as far as an 
overview of what went on and you will get more of the 
written pieces. Yes, Bob? 

Before we ail leave, someone raised a very good process 
question that all of us as a group needs to address and that 
is this information of all the copies we have received and 


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are taking back home to your institutions, to what extent 
should people feel free to make copies to distribute to 
others in their organization? We have been privileged so 
far that given the sensitivity of information, we have been 
able to manage to keep it out of, let’s say, less responsible 
hands, yet the nature of kind of proliferation and Xerox 
machines being what they are, the risk of that changes. So 
I guess as a group perhaps, and Roger, you may have 
thought about that? 

Dr. Bernier: We have not specifically thought. I would take this 

opportunity to remind everyone that we have now been 
working with this information for several weeks. I think 
the fact that we were able to hold this meeting the last two 
days is a direct result of the fact that this information has 
been held fairly tightly. I think it has been a privilege to 
have this meeting and we have other meetings like this. As 
difficult as the science is, there are two other equally tricky, 
complex challenges. The policy crafting has to take into 
consideration some very diverse and complex issues. 
There is another group that will deal with that, and then we 
have the communication and how we handle this, which I 
think I am no expert at, but seems equally daunting to me 
as the scientific and the policy issue. 

I don’t think we can set a rule here because some people 
have gotten these documents. For example, some of the 
manufacturers were privileged to receive this information. 
It has been important for them to share it within the 
company with the experts there, so they can review it. 
Some of you may have questions. You may have given a 
copy, but I think if we will all just consider this embargoed 
information, if I can use that term, and very highly 
protected information, I think that was the best I can offer. 
If anyone else wants to make a suggestion, but I would say 
consider it embargoed and protected until it is made public 
on June 21 and 22 at the ACIP. There is a plan to do that. 


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There are policy groups that will be meeting before this, 
and communications experts that are meeting in advance, 
but until June 21 or 22, 1 think that would be the best way 
to proceed. 

Now that I have the floor, if there is no other comments, on 
behalf of CDC I will take the prerogative of where I am 
sitting to thank everyone on behalf of the Centers for 
Disease Control, and probably on behalf of the Public 
Health Service, on behalf of the National Immunization 
Program. 

If I could get to see some of you personally, I feel I made a 
connection with you when I invited you, but I feel bad that 
I haven’t really continued that. I am looking at Dr. Rapin 
in particular. Dr. Stein. Some of you I haven’t really had 
much opportunity to make contact with once you got here, 
but believe me, I am very grateful for what you did to make 
yourselves available, and I want to say thank you if I did 
not get to talk with you personally. 

It may have been a blessing in disguise that the Super 
Comp Computer Conference was held simultaneously 
because it forced us to come to Simpsonwood, probably the 
only place in Atlanta that had any room. I think it created a 
spirit in this meeting that I think we benefited from. The 
kind of informality and effort to really try to figure out, 
which to me was the biggest challenge, what is the best 
way to understand and think about these observations. We 
really didn’t know that when we came in here. I think we 
made progress as a group. That we have a better idea about 
the best way to understand these data. 

The other thing that I was struck by was the quality of the 
science. Many of you commented very positively about the 
work that was done by the scientists. I am a proud member 
of the National Immunization Program. Prouder after this 


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meeting than I was coming in, and I want to congratulate 
the team at the table. I think you made us all proud. 

The other thing I was struck by was the aura of seriousness, 
an implication that sort of hovered over all of this. 
Although we were all informal and this place gave you a 
feeling of a special spirit, I think overall there was this aura 
that we were engaged in something as important as 
anything else we have ever done. So I think that was 
another element to this that made this a special meeting. 

I also think it has been extremely productive. Despite 
some of the semantic differences and issues that arose, I 
think in the end we stopped talking about that pretty 
quickly, and whoever suggested let’s just keep going 
probably made the best suggestion. I think the questions in 
the end worked and I think we have had a productive 
meeting and that when we look at your notes, we will find 
there is a lot there. 

I wanted to end by mentioning about the policy work and 
the communication work. I have also been struck by how 
much that is going to be as challenging. I have already said 
it, so I won’t dwell on that, but you get the point that this is 
only one leg of a three legged stool and there are two other 
meetings just like this one that should take place, on the 
policy side and the communication side, as those experts 
try to get it right from their perspective. 

Dixie, Walt, do you want to add anything? 

Dr. Snider: Just briefly let me say first of all, thank you very much. It 

has been interesting for me over the past seven or eight 
years in this position to go through folic acid fortification 
and Rotavirus and all the other interesting issues. I think 
this is one of the tougher ones. The fact is that your 
consultations make a tough job a bit easier and we are most 


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grateful to you being willing to come and contribute and 
we appreciate it greatly. 


Dr. Orenstein: 


Dr. Johnson: 


If I can just add my thanks. One to the NIP staff who have 
worked and labored day and night for months to come to 
the presentations. I would like to thank Bob Chen, Frank 
DeStefano, Phil Rhodes and especially Tom Verstraeten. I 
have seen him in audience after audience deal with 
exceedingly skeptical individuals and deal with them in a 
very calm way in answering their questions and doing the 
analyses and I think you are mature well beyond your 
years. 

I would also like to thank Roger Bernier who pulled off 
this meeting in rather short notice, and I think as everyone 
has said, I think this was an excellent meeting and is going 
to be very, very helpful to us, and we appreciate the time 
and effort you have spent. 

In a sense this meeting addresses some of the concerns we 
had last summer when we were trying to make policy in the 
absence of a careful scientific review. I think this time we 
have gotten it straight. We’ve got the scientific review, 
because the policy and communications really have to 
derive from that scientific review. We appreciate all that 
you have done to help us with that and I think we will take 
it forward in working with the ACIP and other groups and 
agencies to try and carry on. 

I would also like to thank Dick Johnston and Paul Stehr- 
Green for being the rapporteur. 

Thank you, Walt. 


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Vaccine Safety Datalink 
Study 




CONFIDENTIAL 


DC NOT COPY OR RELEASE 


Following is an update on the proceedings and findings so Tar of tne nrst phase oi tins 
oronosed two chased srady. I have used the original protocoi as outline tor this update. 


Stud" design: 


Retrospe: 

live cohort su 

tidy using the v 

'accine Safe' 

EHaibf' 

* criteria: 



Eligibility 

• was restric'v 

to children w 

ho meet the 

** 

Bom in IP. 

E cr later. 


-• 

Eligible K' 

O member sir 

e birth (i.e. 

- 

Continue vs 

/ enrolled unti 

1 the first bh 


f ry ?> v 


Hie following children were excluded from the analyses: 

* Premature and severe oremarure children. Prematurity was deiinec as Dirtnweignt o- 
1000-1499 srams or gestational age of 2S - 37 completed weeks. Severe prematurity was 
defined as birth weight of less tnan 1000 g or less than -S completes weeks, vve 
identified these children by the ICD9 coae 765. 

• Children that did not receive two Doiic vaccines by the age o: i. inis conaition was 
set to avoid including children enrolled in the HMO that die not use the services, r'oiic 
was considered the most commonly accepted vaccination. 

t- Children that received henatitis B immune g 1 cbm m. as tnese were more iikeiy to have 
higher exposure and outcome ieveis. 

c Children that had the diagnosis before the age a; whicn the exposure was assessed. 

g Children in whom any major congenital or perinatal prooiem occurred (including any 
unspecified problem involving the cardiac, respiratory or central nervous system). 

e Children that remained longer than 10 days in the birth hospital or were nospitaiizeG 
for am- period over 10 days m me first tnree months o: me. 


02/29/00 



Case definition : 

A case was defined as anv child that was diagnosed with one oi tne neurologic Oi ienal 
conditions, listed in the annex. No distinction was made on whether a diagnosis was 
made in the cunic or no spiral setting. 

Exposure assessment: 

Ace-elated cumulative exposure levels were oenveu from the automated oat a at i and c 
months of age. 

Con founders an d Effect Mod elers: 

Tlie following variables were included in the analyses: HMG site, year ana me mix oi 
birth, gender. 

Statistical analysis : 

We used proportional hazards models for ail risk analyses, stratified by site, year and 
month of birth and adjusted for genaer. 

Tne startooir.t was the date of birth or Jan I s ' 9: for children bom into N\. K oeioie uus 

date fno OPD data available.. f ( S 
The endooint was defined as the fir si o* tne loliowins nates: 

« the date of first diagnosis 

* the firs; date that a child stopped being enrolled in the HMG 

D \ ““ •* St r\ — 

ecemoer o i . / 

"f-jj. diagnoses were an ah-' zed srouoec m categories (neurologic developmental and renal j 
and individually if we encountered at least 50 cases. Because o: tne iownumoer o. 
cases, the heterogeneity of disorders or lack of specificity oi the iCD9 codes 

n £ r r.the- . ^ VrO did not oursue analvses of the “degenerative neurologic ano 
“other neurologic*' categories as a group, but only for tne ioliowmg diagnoses, epilepsy, 
acquired obstructive hydroceohaius and infantile cere oral paisy. 


\j — 


r\ 

~y. 


r\r, 

'•J 



DC NOT COPY OR I 




A separate analysis was done for premature imams with oirthweignt oetween 
as this group was found to have certain vaccination characteristics (yotai mens, 
vaccines in the first year of life, use of Hepatitis 3 vaccine) similar to me gens 
3y limiting to this, group, we intended to avoid the Dias by. indication probieu 
to less exposure ' vaccination) in the group at higher nsk or disease and mus u 
protective effect of the exposure. 

As some diagnoses are often mace in the ciinic setting, we inciuaec ah ± our r, 
additional analyses of autism, sleep disorders, specific developmental ana spe 
anc epilepsy. To evaluate the influence of excluding the children with conge: 
perinatal conditions, we also did the analyses for the category or neurologic 
developmental disorders and the speech delay for ALL infants. 

We anaivzed the cumulative exnosure at 1 anc j months oi age. .-it eacn ag-. 
■'demfv a maximum number of exocsuxe categories with large enough numbe 
and comcarabis size. We then usee the lowest category' as reierem. : mo. 
oniv able to identify two categories for the rare disorders or three categories . 
common disorders. At three months we identified five categories ror most ci 
seven categories for the three most common disorders. 

Sample Size and Power: 

The number of cases for the individual diagnoses variec from i u .:S;. - o 
FT. c: at isast 1. w» restricted the analyses of the individual diagnoses to the 
leas: 50 children. 



INTIDENTIAL 


DC NOT COPY OR RELEASE 


Premature children > ;5QQg: : 

We were able to perform this analysis only for the entire category of neurologic 
developmental disorders. We did not exclude children with congenital or perinatal 
disorders as this would reduce the number of cases to below 50. 

At 1 month of age , we found a RR of 0.89 (0.62, 1.28) and 1.42 (0.62, 3.2S) for exposure 
of 12.5 and > 12.5 ug, respectively, with 0 ug as referent. 

At three months: see graph 15 , 


For all four EM Os: 







For autism and the entire category of developmental delays, the relative risks -found were 
slightly altered: see graph? 16 and 17. For the other disorders with significant-numbers of 
cases in the two added HMOs (sleep disorders, speech disorders, epilepsy), the results 
were similar to those for NCK and GHC separately. 

For A ~_I_ children in all four HMOs: 

For the enure category of neurologic deveioomentai delays none of the exposure groups 
had an increased risk Tee graph 81. 

For the specific group c: speech delays, the relative risk aid not differ from those found 
for the subgroup included ir. the above analyses (see graph 19). 

Discussion: 

We focussed our analyses on the cumulative exposure levels at one and three months of 
age because as this age the central nervous system is still immature and more susceptible 
to mercury. Another reason for this focus was to minimize the difference between the 
dose giver, and the dose actually accumulated in the bod}'. Tne half-life of metnyimercury 
is estimated to be 45 days. If eihyimercury has a similar half-life, the dose given will not 
differ much from the dose accumulated a: one and three months, given that most vaccines 
are given in the second and third month. In addition, the highest proportion of children 
in our cohort exceeded the E?A limits at one and three months of age (see study 
protocol). Whereas the exposure at three months of age is related to later exposure 
(children in high exposure groups will remain in high exposure groups at 6 or 12 months 
of age., this is not the case for exposure a; one me run of age. The main disadvantage 


11’29'OC 



N - idektia 


DO NOT COPY OR REumA: 


with :he 3 months categories is the small number of cases m the lowest groups, 
oanicuiariv the 0 exposure group, which forced us to defme the referent group as me 
categorv below 37.5 us. except for the more common disorders. 

As for the exposure evaluated at i month of age. which is basically an evaluation or the 




n^opcYO.1 heoatius cos--* we have round 2 . sisriincant relate noi p niS 
for misery and unhappiness disorder (ICD9 code 313.1). We were not able to proouce 
a araoh for the RRs at 3 months of this condition as no or few cases occur in the two 
lower catesories. The relative risk ror tins condition was signinc .ntiy incica^cu . . 

95%CT: l .09-3 .SO ) when comparing those with a cumulative exposure aoove o2 > ug at 
three months compared to those with cumulative exposure equal t or less than c 2 .:- ug. 
There is a nearly sign .leant increased risk for the category exceec mg 12.5 ug at • month 
for arieaticn deficit disorder. This group includes children uiat received a dos-v.-. of 
Hen3 or their first dose of Hib or DTP in the first month of life. A*, three months, this 
oositive relationship is no longer significant for any category. 

As for the exposure evaluated at 3 months of age. we found increasing risks oi 
neurologic a eveioo mental disorders with increasing cumulative exposure to tmmerosa.. 
’within the sroun of developmental disorders, similar, thougn not statistically signmcan. 
increases were seen for the sub-group called specific delays (1CD9 code 31 m- and within 
this sub-arout- for the specific disorder developmental speecn disorder laysiaua. 
code 315.39- and for autism (TCD9 coce 299.0). stuttering (I CD 9 code jC. .um and 
attention deficit disorder CCD 9 code inis increase, when comparing each 

cate 20 m’ of exposure to me iowes: exocsurs group was signmcant omy for me entire 
caiecon' of devsioomentai disorders. For specific delays and speech disorder inis 
increase occurs only above 25 up. 

As some of the above disorders are correlates. ; see taoie ■ i we ana iy zee the Krts ior eacn 
while excluding children with any of the other discraers and lounc similar results to me 
unconditional analyses. 



:onfidential 


DO NOT COPY OR RELEASE 


Table 1. Number oi common cases in some disorders 



2990 3070 3140 

C * R C C- 

2990 

■ 

66 | 0 j 7 j 

i ; 1 

T ! 

1 

3070 

: d9 i 2 | 

, ; i 

I i i 

15 j 

i ' 

3140 

| 158 

o 
( 1 

31539 


830 j 

i 


Tor other disorders, the trend of the risk with increasing exposure to thimercsai was 
Nther decrees - ': :? (renal disorders) or unclear (somnambulism, mixed emotional 
disturbances and cerebral palsy). For epilepsy we found a significant drop of the risk 
v,hen exceeding 25 ug, followed by an increasing trend. We pian to evaluate the role oi 
earlier diagnosed convulsions in these children to better understand this rinding. 

To evaluate potential confounding by health care use (to identify- potential sick children 
that may have been more likely to have the disorder and less likely to be vaccinated or. 
inversely, to identify those oarents that bring their children in for minor aiiments and are 
more likely to have their children vaccinated;, we evaluated for each exposure ievei. the 
number of hospital and clinic diagnoses, the maximum length of hospital stay preceding 
the exposure anc the length c: stay in the birth hospital. We did not see any differences 
ir. me frequency distribution of any of these, suggesting that the categories are 
comparable in terms of ore -cxistmo illnesses or Health care seeking oehavior oi the 

We also iookec a: the number of vaccinations (DTP. Hib, HepB anc complete 
vaccination schedule (3 Hib. 3 DTP and 2 Polio, with or without the Hepatitis E 
reouireniend) by the end of the first year of life. The frequency distriouuon or these 
differed for me lowest exposure category, but was similar above 25 ug at three months 
(ex sept for HepB). This suggests that children in the lowest exposure categories get an 
mplete vaccination schedule for reasons not related to health care seeking oehavior. 

^0-0^2= oetvs / ee T ~ r n c ‘ r '' ^'’dcsu^" 5 * categories lies ir me use or neoati tis n 


22/29/00 





DO NOT COPY ORRi 


vaccine, thimerosai free vaccines, combination vaccine of Hits anc DT? or simp 
timins of the vaccinations. We. plan to repeat the analyses stratified by one of tr 
measures of health care seeking behavior and up-to-dateness of immunizations. 

As for orenarure children, we found no associated risk ox neurologic ceveiopm-t 
outcomes to cumulative thimerosai exposure at one or three montns. As we cic 
exclude children with congenital or oerinatai problems, however, tnis analysis n 
be biased. Wnen including all premature children, irrespective or their birtnwei 
found a protective effect of thimerosai above the 25 ug level at three months, su 
an avoidance of vaccination in the most severe group (wnicn is also more xikeiy 
the outcome). Tnis is confirmed when comparing the levels oi vaccination to th 
birthweight groups. — 

When including the children from ail HMOs. we noticed that the increased risk 
developmental neurologic disorders was no longer significant. The two ao-aea • 
have either no outpatient data (SCK) or only since 1996 (NWK) ana many or th 
disorders in this category (emotional disturbances, attention dencii cisoraer. tic; 
stammering, had no or very few cases in these HMOs. which may explain this : 
Tne came for autism., siishtlv differs as most added exposed cases are round in 
manes: exposure categories, .as mentioned oeiore. ror the other aisoraers the it 
were similar tc these for the analyses of the two original HMOs fNCK anc QH< 

Wner. including the children with congenital or perinatal conditions, no mcreas- 
was found fer the broad categories of any or specific aeveiopmenta: aeiays. m 
avoidance of immunisation in infants a: highest risk of developing • 
the specific diagnosis of speech delay this phenomenon did net 


SUSUwSvO 


conditions. r < 

In conclusion, we cun s 12 .it tn zz in is susuvsis- ooss not mie out thsc receipt oi tni 
containing vaccine in children under three months of age may be related tc an h 
risk cf neurologic developmental disorders. Specific conditions that may warm 
detailed studv include autism, dysiaiia. miser}' and unhappiness disorder ana at- 
deficit disorder. There is no indication that thimerosai exposure is linked to inc 
rig’.- o* degenerative or other non-deveioomeata; neurologic discraers or rena; c 


02 29/00 



CONFIDENTIAL 


DO NOT COPY OR RELEASE 


Limitations; 


• Vi's have limited our analyses to a list of potential outcomes cased on prior 
knowledge of adverse conditions found in infants exposed to high doses oi 
methvimercury. We cannot rale out other disorders potentially related to exposure to 
ethylmercury. 

• We were able to evaluate only relatively severe conditions tnat come to mecucai 
attention, and not possibly more subtle effects that would require neuropsychological 
testing. 

• The stud*' was underpowered for some conditions, particularly the renal outcomes. 

« Some miseiassification errors may have occurred in the exposure assessment (some 
vaccinations, particularly the neonatal HepB dose may not have been reported! 

« We were not able to differentiate between single dose thimerosai free hue vaccines 
and muiti-aose thimerosai containing Hie vaccines. Tne analyses were done assuming 
all vaccines to come mom multi-dose viais. An analysis assuming ail riib vaccines to 
come from single dcse-viais die not substantially alter tne results. 

• We had no information on some potential confounders. suen as maternal smoking or 
fish consumption. 

« We could not differentiate between the difference in effect from the preservative o: 
active component in the vaccines. Exposure to thimerosai from vaccines is invanaoiy 
linked to the likelihood of being vaccinated with Hepatitis E. DTP or Hie.. 

« We reiiec entireb' or. automated data and die not control its quality, mis is assumes 
tc be hisn for most data, but mavoe .ess sc- ior birtpweigrt and/or gestational age. 

Proposal for future study 

.As we do not expect to sain substantially more or dirferent information from venncation 
of the current firdinss through chart abstractions or case-control study, we propose to 
conduct a foilow-uu study of current of the neuropsychoiogic functioning oi cohorts 
children randomlv draw- front different exposure categories. 



roNI-fDF'.NTlAf 


c 


DO MOT COPY OR R! 



Tsilih* Number of 


! children HU-nUlied per dism der and disli ilnilion l*y stile, ponder, year ol In« 1I» imd pro m a lardy 


r ~ i 

* — ■ — ‘ 




Description 

Tola! 

Nnl exrl 

1 

\JUamT ~ ” ~~ | 

F 16229 

76509 

17 

1 Inuologic (. 

egenerative disoi tiers: 

145 

’ 330 .x 

.i.ii.x 

333.x 

33 i.x 

335 x 

Cerebral degenerations usually 

Other cerebral degenerative disease 

Other cxlrapyrnmidal disease and 
Spiiiocereliellai' disease 

Anterior horn ceil disease 

7 

59 

47 

8 
\ 

4 

M 

n 

1 

3 

17' iT 

( n ofoj'.ic tic vf*lo| >« noiilaf cl ir;;ihil il j 

.)<)') i 

290.0 

209 8 

299 9 

307.0 

307.2 

307 J 

307/1 

307 5 

J0 /.ft 

m 

3ld.fi 

Vis 

Ji53«> 

3 i V) 
317-319 

Autism 

Oliici chiiJiiooil psychosis 

Ollier tinspecif ictt psychosis 

Stammering & sliillciing 

Tics 

Repetitive movements 

Sleep disorders 

Rating disoi dels 

Rmuosis 

Disturbance of emotions specific to 

Attention deficit Sy 

Specific delays in devciopmenl 

Developmental speech delay 

Unspecified developmental delay 

Mental retardation 

|l)9 

39 

17 

80 

70 

2 

121 

85 

if) 

T\\ 

24 8 

2 1 M 

i 210 

813 

50 

67 

22 

17 

59 

33 

2 

81 

35 

3 

1 50 " 

158 

1235 

8i i 

363 

12 



tlT , 


( 'ode 

Description 

Total 

1 Jot CXI 1 

1 

256 3 

1 Other neurologic conditions: 

~687 

fTilx 

| Infantile cerebral palsy 

‘289 

7)1 


fli,i i| 

'sile( n /{ 

.) 

~Scx (%) 

■ 

Year o 

f birth 

(%) 

C 

A 


Arc * 

c 

vv 

r 

m 

92 

93 

94 

95 

96 

% Pi oin 



IT 

50 

50 

17 

21 

19 

19 

22 

2 

22 

67 

33 

33 

37 

21 

19 

33 

7 

12 

0 

TT~ 



*25 

50 

50 

25 

25 ' 

0 

0 

50 

0 

16 

64 

36 

43 

57 

14 

29 

29 

7 

2i 

d 

28 

6-i 

36 

29 ' 

71 

29 

18 

29 

4 

i j 

0 

23 

29 

67 

67 

33 

33 

33 

33 

67 

"67 

33 

33'" 

0 

33 

33 

33 

33 

0 * 

0 

0 

d , 

0 

77 

52 

TV 

35 

65 

22 

27 

24 

17 

8 

4 

30 

85 

15 

13 

87 

30 

39 

25 

6 

0 

7 

n 

86 

14 * 

9 . 

91 

41 

32 

18 

9 

() 

0 

32 

0 

loo”' 

12 

88 

47 

12 

35 

6 

d 

() 

39 “ 

35 

55 

32 

68 

24 

37 

34 

5 

0 

d 

36 

53 

A3 7 

40 

60 

35 

23 

19 

16 

7 

0 

20 

.. .. . 

ioo 

50 

50 

50"" 

d 

50' 

0 

0 

o | 

26 

53 

47 

‘41 

"59“ 

10 

32 

21 

"?T“ 

16 

[ 1 

21 

96 

r _ 

42 

T8 “ 

13" 1 

36 ! 

"33 


7 

0 

53 

100 

0 

25" 

75 

75 

25 

o 

1) 

0 

d 

ii\ 

25’ 

75 

43 

57 

32 “ 

30 

2l , 

9 

5 

i 

3 J 

73 

26 

22 

78 

4“ 

34 ' 

ITT 

- T~ 

6 

d 

C 

27 1 

50 

50 

33 

67 j 

20" 

27 

' 25 ' 

f9 

8 

J 

32 

56 

44 

29' 

71 

20 

3(1 

29 

1 8 

4 

2 

21 

33 

57 

40 

60 

T5 

20 

2i 

23 

17 

9 

33 

83 

67 ‘ 

"33” 

67 


33 

17 

8 

0 

8 

* — * 

Site ('! 

'•) 

Sex (% 

) 

j Year of birth 

(%) 





Age- * 

r 

Wl 

! f 

5 

F~ 

L 

92. 

93 

| 9/1 

[L 

96 

% IVem 

23 

1T~2 

17 

43 

lj 7 

25 

2.3 

nc 

l‘> 


4 

22 

1T~ 

23 

* rm ** 

lii. 

59* 

27 

' 13 

Ul_ 

111- 

’TP 

7 


\ 



(’( >N)'inHNT!AL 


( 


I )< ) N< VI' corv ( >U RBI .BA 




TsiM** .V’SrtiitpIc si/e jiih! relative ns Us for grouped sum! spocilic disonh'is, Intscd oil tuiiiiihilivo iticiciny c k p omj 3 c <i( t month 


mi ;» j»r 


( '(tile 

Dcscriplinn 

Cases 

UK i 95% ( !! (Rci‘. - 0 jig) 




12.5 pti 1 

> 12.5 pg 

Homo logic deveiopmcnlnl tji.sal »i li 1 ic:i: 

ini 

1.08(0.96, 1.2 1) 

6.87 (6.60, 1.27) 

29' VO 

|~Au(ism 

67 " 

0.96(0.55, i .6*0 

j .58 70.48. 5.20) j 

107.0 

Slammer ing A. slnllcring 

59 

0.97 (0.52, i .79) 

, No cases 

16 7/1 

!■-- ; 

i Sleep disorders 

HI 

0.77(0.45,1.29) j 

' S .74 (6.53, 5.73) 

n i 

1 fistmlmnee ofcmntimi.s specific In 

1 50 

M l (0.93,2.27) ! 

1 .07 (0.26, 4.45) 

Jin 

Misery and unhappiness disorder 

ki 

' 2.68 "(1.29, 5.55) ! 

Mo eases 

Vj Mt 

Mixed emolionnl distmhnnrr:; 

5 7 

0.74 (0 78, 1.4-1 } 

! .48 (0.35, 6.23) * 

j i i o 

Alienlion defieil Sy 

1 58 

0.96 (0.65, i A 1 ) 

2.14(0.99, 4.62) j 

3 1 5 ! 

- - 

Specific delays in dcvolopmcnl 

i 9 75 

i .06 (0.92; i 22) 

0.76 ((1477 i .27) 

.715.79 | 

1 Vevelopmenlai speee|> delay 

8 17 

I ll (0.957173 1) 

j 6 80 (0.46, T.39) 

3 i 5.9 ‘ 

Unspecified delays in developmenl 

298 

1 .00 (0 767 1 32) 1 

| 0.69(6.25, 1.87) 

l 

< )liu % i neurologic conditions: 

256 



vi Vv 

Infantile ociehtal palsy 

IT 

0.93 (6.49, 1 .76) | 

| 0.81 (6.1 1, 6.05) 

Vi 5 

h'.pilepsy 

i 27 

j .26 (0.84, 187) 

j (17 8 (67i 9, 3.21) 

Renal conditions: 

99 

0.99 (0.9-1, 1.52) 1 

j 0.36 (6.05, 2.65) 

5919 

l Jnspecificd disease of kidney 

“56~" 

YA50l7A2.‘im j 

\ 'Mo cases 


+ Hist diagnosis, in months 


i 


o?/;. ( >/no 


VSI >np(l:ilr 




!•( Ml iAl 


( 


IK) NOT COPY OK RCLi-aI,. 


:M5 

hpiicpsy 

287 

m 

24 

, 88 

"12 

pi* 

59 

20 

26 

20 

20 

12 

■? | 

T/j () 


In 

Y\ 

19 

76 

24 

48 

52 

48 

29 

19 

5 

n 

5 1 

348.x 

j Other conditions of drain 

39 

19 

19 

53" 

47 

37 

63 

21 

26 

16 

21 

o" 

5 

349.X? 

j Toxic encephalopathy 

0 













.id 9.0 

j l hi sped (ted disorders of net vons 

r»3 

•;>9 

3 i 

j 76 

2d 

34 

66 

31 

iv 

31 

14 

3 

0 

J 356 .x 

| Idiopathic polyneuiopnthy 


i 

23 













357.x 

Oilier polyneuropathies 

0 













3.58.x 

Toxic and other myonenial 

7 

3 

17 



33 

67 

67 

33 

33 

67 

0 

0 

0 

0 

359.x 

1 Toxic and other myopathies 

! 1 

5 

29 

j ioo 

0 

20 

80 

40 

20 

0 

0 

40 

0 

1 Renal conditions: 

m 

99 

22 ' 

i. ' 

jL_. 

; 48 

52 

23 

22 

»9 

To 

15 

3 

7)80 

Acute glomcuifoncpin it is 

r 

v ~ 

75 ] 

| | 


I - *" - * 

r 







1 

581 

Nephrotic. Sy 

iv 

13 


i li#0 

V j 

? 31 

69 “* 

1*6 

15“ 

8 

23 

8 

d 

582 

Chronic glomcmloneplu id's 

8 

5 

20 \ 

j 0 

100 

20 

! 80 " 

w 

20 

20 

20 

! 0 

o 

583 

Not sped lied as d neplimpnlliy 

56 

31 

19 

90 

io 

55 

pi 5” 

32 

26 

26 " 

id 

6 

o 

584 

Acute renal Ini hue 

id 

5 

31““ 

80 

j 20 

60 

4(7 

40 

2(1 

0 

, 6 

AO 

d 

585 

Chronic renal failure 

9 

3 

51 

67 

; yi 

o 

r Too 

100 

0 

d 

o 

d 

d 

586 ] 

Unspecified icnal fnilnic 

i i 

! r? 

25 

: 57 ' 

1 5 

i 29 ' 

71 

T4 ~ 

29 

T 

T) 

57 

0 

593.9 ] 

linspccillcd disease of kidney 

i 06 

| 56 

I2L—1 

L”_J 

13 

gz 

54 

I 

21 

“Tr" 

*23' 

Id 

5 


* nl lirsl diagnosis, in months 

I I he number not excluded by eliminating congenital ami pciinnfnl disorders 



o?./ 29 /nn 


VS I )uprlnle dor 


12 































Graph 1: Relative risk - 95 % Cl of Developmental neurologic disorders after different 
exposure levels of ihimerosai at 3 months cf age, NCK &GHC 




Grapr. 2: Relative risk - 95 % Cl of Kena; disorders after different exposure ieveis of 
Ihimerosai at 3 months of age. NCK &GHC 




CcmDtP.3d2.xis 


2/2S/0C 



Graph 3: Relative risk - So % C! of Autism after different exposure ieveis of 
thimerosai at 3 months cf age, NCK &GHC 



< 37.5 uc 37.5 ug (n=l6'» 50 ug £2.5 uc (n=o) > 62.5 ug (n=t3) 

Cumuiativs mercury exposure (and numder of exposed cases(n)) 


Graph 4: Relative risK «*- 95 % Cf of Stammering after different exposure ieveis of 
thimerosai at 3 months of ace 7 NCK &GHC 





Graph 7: Relative risk SS % Cl cf O ther or mixed emotional disturbances of childhood 
and adolescence after different exposure levels of thimercsai a: 2 months cf age, NCK &GHC 




Comb?ned2. xis 



Graph 9: Relative risk - 95 % C! of S pecific rieiavs in development after different 
exposure ieveis of thimercsai at 3 months of age., NCK &GHC 






•0mbinec2.xjs 



Graph 12: Relative risk -r 95 % C/ of Infantile cerbral aaisv after different exDosure ievels of 
tnimsrcsai at 2 months of age, NCK &GHC 



; uc in* . • ' ii.; uc :n=_ 
Cumulative mercur 


3, £0 uq n*25‘- £2.5 uc (P=t2' >52.5 uc (n= 

y exposure jst.c numoer c: exDoseG cases m)} 


Graor 14: Relative risk - 95 c k C ' of Urtsoecinec kicnev or ureter disorder after different 
exposure levels of thimsrcsa; s: 3 months of aae. NCK &GHC 



?;neo^.xis 


2/2S/00 



Graph IS: Relative risk - 95 % Cl of Developments! nsumionir disorders smono 
prematures f>1500 g> after different exposure levels of thimerosa! at 3 months of age 



Graph 16: Relative risk + 95 % Cl cf Autism after different exposure levels 
of thimerosa! at 3 months of ace for all HMOs 



.c ug ir» s 3; 2 .c- uc (n=1 7 50 ug in— 22' £2.5 uc (n=i4' > £2.5 uc 1 

Cumulative mercury sxnosure ianc number of exoosea cases in}} 


Graph 17: Relative nsk ~ 9c % C! of Development neurologic disorder? 
after different exposure levels of thimercsai a; 2 months of age for all HMDs 



0 uc 
(n=2S) 


i l . c ug 
(r=22) 


uc 

(r-25 


.c uc: 


50 uc 


uc 

(n=2iV; 


> c2.5 uc 
(n=5S5; 


Cumulative mercury exposure (and number c; exDOseri cases {nj; 



Graph IE: Relative risk + 95 % C! of Developmental neurologic disorders after 
different exposure levels of thimerosai at 3 months of age for ALL kids, at! HMOs 



Cumulative mercury exposure (ana number of exposed cases (n» 


Grapn IS: Keiative risk - 95 % Cl of Deveioomental speech disorder after 
different exposure levels of thimerosai at 3 months of age for ALL kids, all HMOs 



0 UC 
tn=22' 






Cumulative mercury exposure ianc numoer of exoosec cases in); 






Ctur.MlnHv'! moinny ovpnsm** (*U|) 


V accine combinations m me cumulative 

o o a • 


rcurv sxocsure categories a: cores montns o: 


Category 

""P T H A r V* 

j. a wU UU1J.W- : 

Combinations 

0 tie 

2% j 

No vaccines 1 

12.5 ug 

2% ! 

1 HepB only 1 

25 yg 

A 0 / 

4/0 

2 KepB. 0 DTP. 0 Hib (25%) j 

1 HepB - Hib, 1 DTP (25%) | 

0 HenB, 1 D7?-Kib (50%) j 

i 7 * * 0 

" 1 Q/ 

J 1 /C 

1 HenB, 1 DT?-Rib 

50 ug 

j ^ 

32% 

2 HtoB. 1 DTP-Hib (75%) 

| 0 HepB. 1 DTP. 1 Hib (25%) : 

j 62.3 1:2 

i 2% 

1 HrpB, 1 DTP. 1 Hid 

> 62. ug 

: 1 c*% (0-- **° .O 

fTheoB, 1 DTP, 1 lib 

Note- DTP includes 

■’.T <Vp 

- 

Mercury contents 

■:) KeoB: 12.5. QT?(D 

Tap): 25, Hib: 25*, DTP-Hib: 25. HepB- 


rJ. C ' D 


\vc assumed 3.11 Hio 10 dc rrom rnuixi~o.es 3 visis (tliiiiisros&i contaiiiiiig. ■ 


Frequency of categories of 


cumulative me 


u rv sxocsure at three months of age. oy oirinyear 




Thimerosai VSD study- Foliow-up on conference caii 03/02/2000 

This report summarizes additional analyses I did as a resuit of the many suggestions 
received during the mentioned conference call. 

As the outcome “neurologic developmental disorders” seems to provide a reasonable 
summary of all important outcomes (in terms of sample size). I have restricted the 
following analyses to this category’ of outcomes. 

Also fcr sake of reducing the number of analyses, and to keep the results easier to 
interpret, I have used the cumulative exposure at three months as a continuous variable 
T his also resolves the problem of which reference category to choose. 

This follow-up report addresses the following issues: 

•9 .Ascertainment of birth dose HepB 

* Socio-economic status ~ 

« Health care seeking behavior: 

* Adjustment for age 

• Data from NCK before 1995 

The following are responses to correspondence after the conference call 

♦ Control diagnoses 

* Comparison to number of vaccines, aluminum 

• Thimeresai content of Hib vaccines 


•“pj'i Tyr^i *arr pvrpr ^ — Aa-r> 

idiifJ.A.j.'wii - W _ W u U*.. Udwv __ 


On a request oy rsoc n?av:s xc give an :aea on me accuracy or tat cinn cose xor nept. m 
the automated data. NCK estimated the capture of the birth dose to be in the high 90% 
range from 7/91 onwards. GHC also expressed confidence in their capture of the birth 
dose from 1 0/92 onwards. 


1 tried to estimate the proportion of missed birth ooses : assuming that these were missed 
if the automated data suggested that a child, continuously enrolled in the first two years, 
had had only two doses of Hepatitis B by the age off years, but ail the four DT? and Hib 
and three polio vaccinations. This approach suggested that the birth dose was not 
registered in 3.8% and 16.5% at NCK and GHC respectively 

Alternatively. I looked at children continuously enrolled in the first year that had only 1 
dose of HepB by six months, but were or. schedule for DTP. Hib and Poiio (at least two 
of each). According to this analysis. 4.2 % and 17.9% of birth doses are missed at NCK 
and GHC respectively. Over the years there is a stead;-' improvement at NCK from 5.9% 


- v? *,*/>*» "V C T>f 


Respo Rst.dcc 



ic 5.2%, whereas at GHC, there is an improvement after an initial decline (11.8%, 22.2%. 
25.9%. 15.6% and 13.6% for ’92. ’93. ’94, ’95. and ’96 resnectivelv). 


These data are comparable 
Although these rates are re 
thimerosal analysis as only 


to findings in John Mullooly’s paper on data quality, 
latively high at GHC, they probably have little effect on the 
' 12.5 ug of sthylmercury to the cumulative dose is added for 


eacn rieois vaccine. 


_. bocio-eccnorme status 


I linked the files to 1990 census data on blocks of homes. I then assigned race and 
income tc the children according to which was the most prevalent in their block (e.g 
60% was white. 30% black, 5 % asian etc, I wouid call the child white). Doing this: 
obtained the following distribution of race and income; 


® K ace: 

While 8 3 % 
Hispanic 6.9% 
Asian 6.5% 
Black 3 .7% 
Native American 


n no.,/ 

V . / yj 


«■ . early nousenoic income 

Under 15 K: 6.8 % 

- r <1 ^ T-- . ' ' -j ’ 

. . J.** iv. ... .■ C 

25 - 49 K: 66.9 % 

5C- 7 4 K: 10.8 % 

75 K: 14.3 % 


. loune no association between the ievei or income anc mg exposure leveis (at Hirer 
months). Although there was a slight increase of exposure among whites and Asians 
(average Hg exposure a; 3 months 50 ug anc 49 ug vs. 46 ug and 47 ug among biacks 
and Hispanics: and an increased chance or the outcome among whites, stratification by 
race or income did not chases the RR estimates. 


seemnc oenavior: well 

TA -ot-o • •< ■ 




+ k. V wLm \J V- • 1 - UilC " l- J V 

rth HMDs. For those aooroximateiv \ Q% of chiidre: 


in wmcn it is recorded, mere was no cunerenoe across me strata o: exposure in ms 
number of well child visits 


4. Adjustment for age (Check of proportionality assumption) 

As age is equal to time in the PH model, adjusting for age is equivalent to checking the 
proportionality. In a stratified model one needs to check the assumption in the strata. 
Since the moosl uses over 100 strata, it wouid be imocssibie however tc check this 




LKLK 03 / 2 S/ 0 C 


PvSSDor.se. coc 



formally for every stratum. As an alternative I did subanaiyses for the different yea 
age at which a child was right censored because of either diagnosis or stopped 
enrollment. 

For all ages tills gives: RR 1.006 (1.004. 1.010) 

Under 1 year: 1.006 (0.985, 1.027) 

1—2 years: 1.010 (1.000, 1.020) 

"2-3 years: 1.007 (0.999, 1.014) 

3 -4 years: 1.009 (0.999, 1.019) 

>4 years: 1.002 (0.990, 1.014) 


ns or 


: appears to be a decline in the RR after 4 years of age, but a rather constant RJ 
before that. 


last 


As an alternative to the ?H model, I also ran a logistic regression model, including 
sender, site, year and month of birth as co variates, exposure measure and outcome as ir 
the ?H model, imposing a minim um age of continuous enrollment lor non-cases 
(imoosing the same minimal age o? diagnosis on cases removed too many or them and 
the model would no: converge). 

Tne RR thus obtained v/as 

1.007 fl .002. 1.01 1) for no minimal time of enrollment 
1.009 (1.004, 1.014) for minim al 3 years of enrollment 

1.008 (1.001. 1.014) for minimal * years of enrollment 


1 concmae teat tne m moae. Goes not aep 
orooerdonaikv assumption is valid. 


;nd on age (.a: least oy years : anc tnat tne 


_jata from NCK before 1 99 


The NCK group is currently checldng for a sample of the 
31539' on the date o? diagnosis. 


rases or soeecn 


c: scree: ±k 


o. uontro: mannoses 


i iooKsc at tne reiationsrup oetwesn tne exposure anc 
which one would not expect a relationship to exist: 


numcer or ireouent outcomes ror 


* Unspecmec conjunctivitis 
« Nonspecifiec. noninfectious diarrea 
© Unspecified ini urv 


For the first two there was no trend o? increased/decreased risk with increasing 
(thimerosal) exposure. For injury the exposure shows a significant protective effect (RR 
decreases 3 % per ug of additional cumulative mercury exposure at three months.;. The 
relative risk? for the different exposure categories are attached in Graph 1 . 


LKLK03/28/OC 


Response. aoc 



7. Comparison to number of vaccines, aluminum 


The purpose of these analyses would be to differentiate between the effect of thimerosal 
and the vaccines themselves. Unfortunately (nearly) all vaccines in our analysis were 
either thimerosal containing (DTP, DTaP HepB and Hib) or thimerosal free (polio). Any 
analysis of the number of vaccines or aluminum as an exposure variable would show a 
correlation to the thimerosal analysis and not be helpful in the distinction. I ran anlayses 
with the number of Hib, DTP, HepB and polio vaccines as exposure and found a 
relationship of the risk to the number of DTP and Hib vaccines received at three months, 
which was to be expected. I also found a relationship to the age at which the first Hib 
vaccine was given (the later the vaccine given , the less chance of neurologic 
developmental delay), which was also to be expected. Surprisingly. I did not find t iis for 
DTP. 

To easily differentiate between the effect of thimerotul and vaccine, we would nc to 
compare a group that received thimerosal free vaccine to thimerosal containing \ . . one, 
which leads to point 8. The closest we have come to such a comparison was by 
comparing the group that received the DTP-Hib combination vaccine (containing 25 pg 
of mercury) to the group that received the DTP and Hib separately (each 25 pg of 
mercury). This comparison showed no significant relation to the outcome neurologic 
developmental delay. 

8. Thimerosal content of Hib vaccines: 

The FDA is currently matching the lot numbers to information on the exact or mean 
thimerosal content for all vaccines used in the two HMOs. 


LKLK03/28/00 


Response.doc 



Graph 1. Relative risk + 95 % confidence intervals of after different exposure leveis O; 
thimerosal at 3 months of age for some additional conditions 

Unspecified conjunctivitis 



0 ug 12.5 ug 25 uc 37.5 ug 50 ug 62.5 ug > 62.5 ug 
(n=366) (n=538) (n=6Q3) (n=6069) (n»53S8) (n*12-17) (n=24S4) 

Cumulative mercury exposure 


Non ; pacific non-ir factious Gash. .-enteritis 



Unspecified injury 



LKLK03/28/00 


Response.doc 




OiN r IDENT IaL 


DO NOT 




OPY OR 


RELEASE 


Thimerosal VSD study 
Phase I 



Update 

2/29/00 


homas Verstraeten. Robert Davis, Frank DeStefano